Fuel economy Research Papers

2007, Climate change

Transport activity, a key component of economic development and human welfare, is increasing around the world as economies grow. For most policymakers, the most pressing problems associated with this increasing transport activity are... more

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Polymer, metal, and ceramic matrix composites for advanced aircraft engine applications

by Khaled Elbahtity

1986, Journal of materials for energy …

Figure 16, - Moriolithic SiC ceramic rotor for AGT engines.

Figure 7. ~ Specific impulse at varlous Mach numbers at op- timum altitude for different propulsion systems,

gure 6, - Types of aircraft engines used for different flight envelopes,

Figure 9, - Comparison of typical airstream gas temperatures along core flowpath of advanced subsonic turbofan and supersonic turbojet engines,

Figure 10,- PMR approach for composite fabrication,

Figure 12, - Graphite fiber/PMR-15 composite outer duct installed on an F404 engine (composite duct located directly above carriage in square- outline region),

Figure 11, - Graphite fiber/PMR-1; composite outer duct for F404 engine (29.5 in diameter x 41,3 in long x 0,09 in wall thickness),

Figure 13, - Comparison of production titanium F404 supersonic fan blade with experimental boron/aluminum blade,

Figure 14, - Tungsten fiber reinforced superalloy composite turbine blade,

Figure 15, - Tungsten fiber reinforced copper rocket combustion chamber,

Table 1. - Applications of PMR=15 Composites Figure 1, - Development of polymer matrix composites for airframe applications, (Ref. 2),

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Transportation and its infrastructure

by Michel Beuthe and

2007, Climate change

Transport activity, a key component of economic development and human welfare, is increasing around the world as economies grow. For most policymakers, the most pressing problems associated with this increasing transport activity are... more

Figure 5.3: Projection of transport energy consumption by region and mode Source: WBCSD, 2004a.

Figure 5.4: Historical and projected CO, emission from transport by modes, 1970-2050 Carre: IEA 2NNG- WReSN 29NNAh

Figure 5.5: Total stock of light-duty vehicles by region Source: WBCSD, 2004a. Civil aviation is one of the world’s fastest growing transport means. ICAO (2006) analysis shows that aviation scheduled traffic (revenue passenger-km, RPK) has grown at an average annual rate of 3.8% between 2001 and 2005 despite the downturn from the terrorist attacks and SARS (Severe Acute Respiratory

Figure 5.7: Historical and projected CO, emissions of seagoing shipping, 1990- 2050 Note: See Table 5.2 for the explanation of the scenarios. Source: adapted from Eyring et al., 2005a,b.

Figure 5.9: Comparison of cost for various biofuels with those for gasoline and diese Source: IEA, 2006b.

Figure 5.10: Reduction of well-to-wheels GHG emissions compared to conventionally fuelled vehicles Note: bars indicate range of estimates. Source: IEA, 2004c; EUCAR/CONCAWE/JRC, 2006.

Figure 5.11: Well-to-wheel CO, emission for major pathways of hydrogen with some estimates of hydrogen production cost (numbers in parentheses) Source: Toyota/Mizuho, 2004; NRC/NAE, 2004.

Figure 5.12: Comparison of three studies on Well-to-wheels CO, emission analyses Note: See text for an explanation of the legend. All the results are normalized by the value of ICE-G (gasoline). Source: EUCAR/CONCAWE/JRC, 2006; GM/ANL, 2005; Toyota/Mizuho, 2004.

Figure 5.13: Projections for transport GHG emissions in 2020 for some cities of developing countries A LaAtaas: f AMAA AE tam T Ar ONON axnnnvine

Figure 5.14: Two possible scenarios for GHG reductions in Light-duty vehicles Source: IEA, 2004b.

Figure 5.15: The effect of a scenario postulating the market penetration of all technologies Source: WBCSD, 2004a.

Figure 5.16: Light-duty vehicle CO, emissions for three scenarios

Figure 5.17: Modal split for the cities represented in the Millennium Cities Database for Sustainable Transport by region Source: Kenworthy & Laube, 2002.

Note: all the fuel economy targets represent test values based on artificial driving cycles. The standards in the EU and Australia are based on voluntary agreements. In most cases, actual on-road fuel economy values will be lower; for example, the US publishes fuel economy estimates for individual LDVs that are about 15% lower than the test val-ues and even these values appear to be optimistic. Miles/gallon is per US gallon.

Figure 5.19: Co-benefits from different mitigation measures in Santiago de Chile Note: toll is applied for cars/busses to enter downtown area or inside the Americo Vespucio ring around the city. Source: Cifuentes and Jorquera, 2002.

Source: WBCSD, 2004b. Table 5.1: World transport energy use in 2000, by mode

Worldwide F-gas emissions in 2003 were reported to be 610 MtCO,-eq in IPCC (2005), but more recent ADEME date (ADEME, 2006) was about 310 Mt CO,-eq (CFC-12 207, HFC-134a 89, HCFC-22 10 MtCO,-eq), which is about 5% of total transport CO, emission. It can be seen that non-CO, emissions from the transport sector are considerably smaller than the CO, emissions. Also, air-conditioning uses significant quantities of energy, with consequent CO, emissions from the fue! used to supply this energy. Although this depends strongly on the climate conditions, it is reported to be 2.5-7.5% of vehicle energy consumption (IPCC, 2005). Non-CO, emissions (CH, and N,O) from road transport in major Annex | parties are listed in UNFCC GHG inventory data. The refrigerant banks and emission trend of F-gases (CFC-12 + HFC-134a) from air-conditioning are reported in the recent IPCC special report on Safeguarding the Ozone Layer and the Global Climate System (IPCC, 2005). Since a rapid switch from CFC-12 to HFC-134a, which has a much lower GWP index, is taking place, the total amount of F-gases is increasing due to the increase in vehicles with air-conditioning, but total emission in CO,-eq is decreasing and forecasted to continue to decrease. Using the recent ADEME data (2006) on F-gas emissions, the shares of emissions from transport sectors for CO., CH,, N.O and F-gases (CFC-12 + HFC-134a+HCFC-22) are:

Note: The fuel S percentages refer to values assumed in (2020/2050). Source: Eyring et al. 2005b. Table 5.2: Summary of shipping technology scenarios

Source: EUCAR/CONCAWE/JRC, 2006, GM/ANL, 2005 and Toyota/Mizuho (2004). Table 5.3: Reduction of Well-to-wheels GHG emissions for various drive train/fuel combinations

Table 5.4: GHG Emissions from vehicles and transport modes in developing countries Note: All numbers in this table are estimates and approximations and are best treated as illustrative. a) Ranges are due largely to varying mixes of carbon and non-carbon energy sources (ranging from about 20-80% coal), and also the assumption that the battery electric vehicle will tend to be somewhat smaller than conventional car b) Hydrogen is assumed to be made from natural gas. c) Assumes heavy urban rail technology (‘Metro’) powered by electricity gener. ated from a mix of coal, natural gas and hydropower, with high passenger use (75% of seats filled on average). ee

Note: Assuming 20% of the urban population uses the BRT each day. a) Operation and maintenance (O & M) costs are expected to be high as the measure involves high demand for management and implementation beyond putting up the infrastructure. Source: estimate based on Bogata CDM Project (footnote 27) Table 5.5: Cost and potential estimated for BRT in Bogota

Source: Wright and Fulton, 2005. Table 5.6: C0, reduction potential and cost per tCO, reduced using public transit policies in typical Latin American cities

Table 5.7: Weight breakdown for four kerosene-fuelled configurations with the same payload and range Source: GbD, 2001.

Table 5.8: Summary table of CO, mitigation potentials in transport sector taken from several studies

Note: EECCA = countries of EasternEurope, the Caucasus and Central Asia. Table 5.10: Regional and worldwide Light-duty vehicle CO, emissions (vehicle only) and fuel consumption, efficiency and reference cases

Note: EECCA = countries of EasternEurope, the Caucasus and Central Asia. Table 5.11: Cost of CO, reduction in new 2030 LDVs

Table 5.12: Economic potential of LDV mitigation technologies as a function of world oil price, for new vehicles in 2030 breakthroughs, biofuels use in road transport would double compared to the Alternative policy scenario.

Table 5.13: Summaries of CO, mitigation potential analysis in aviation IEA cites the following estimates for biofuels CO, reduction when used as a replacement fuel: Corn in the U.S., -13%; ethanol in Europe, -30%; ethanol in Brazil, -90%; sugar beets to ethanol in Europe, —40 to -60%; rapeseed-derived biodiesel in Europe, —40 to -60%.

Table 5.14: Taxes and pricing in the transport sector in developing and developed countries Source: Adapted from Pandey and Bhardwaj, 2000; Gupta, 1999 and European Natural Gas Vehicle Association, 2002.

Table 5.16: Potential energy and GHG savings from pricing, taxes and charges for road transport

Table 5.15: /mpact of a permanent increase in real fuel prices by 10% As an alternative to fuel taxes, registration and circulation taxes can be used to incentivise the purchase (directly) and manufacturing (indirectly) of fuel-efficient cars. This could be done through a revenue neutral fee system, where fuel-efficient cars receive a rebate and guzzler cars are faced with an extra fee. There is evidence that incentives given through registration taxes are more effective than incentives given through annual circulation taxes (Annema et al., 2001). Buyers of new cars do not expect to be able to pass on increased registration taxes when selling the vehicle. Due to refunds on registration taxes for cars that were relatively fuel efficient compared to similar sized cars, the percentage of cars sold in the two most fuel efficient classes increased from 0.3%-—3.2% (cars over 20% more fuel efficient than average) and from 9.5%—16.1% (for cars between 10 and 20% more fuel efficient than average) in the Netherlands (ADAC, 2005). After the abolishment of the refunds, shares decreased again. COWI (2002) modelled the impact on fuel efficiency of reforming current registration and circulation taxes so they would depend fully on the CO, emissions of new cars. Calculated reduction percentages varied from 3.3-8.5% for 9 European countries, depending on their current tax bases.

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Reducing GHG emissions in the United States' transportation sector

by David Andress and

2011, Energy for Sustainable Development

Reducing GHG emissions in the U.S. transportation sector requires both the use of highly efficient propulsion systems and low carbon fuels. This study compares reduction potentials that might be achieved in 2060 for several advanced... more

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A review on belt and chain continuously variable transmissions (CVT): Dynamics and control

by Imtiaz Haque

2009, Mechanism and Machine Theory

Over the last two decades, significant research effort has been directed towards developing vehicle transmissions that reduce the energy consumption of an automobile. This effort has been a direct consequence of the growing environmental... more

Fig. 1. Metal V-belt CVT drive: (a) basic configuration; (b) belt structure [15]. | le. enaiae Oe Oo The basic configuration of a a ‘CVT comprises two variable diameter pulleys kept at a fixed distance apart and connected by a power-transmitting device like belt or chain. One of the sheaves on each pulley is movable. The belt/chain can undergo both radial and tangential motions depending on the torque loading conditions and the axial forces on the pulleys. This con-

Fig. 2. Chain CVT drive: (a) basic configuration; (b) chain structure [16,72].

Fig. 3. Geometrical description of a belt CVT drive.

Fig. 5. Pulley deformation model: (a) axial deformation; (b) pulley skewness [44].

Fig. 4. Kinematic description of a belt element on the pulley wrap.

Fig. 6. Slip mechanism due to elemental gap redistribution in a metal belt CVT [34]. Sferra et al. [38] developed a unique model of a metal V-belt CVT in order to simulate its transient behavior. The model included inertial and pulley deformation effects. Discrete and continuous shifting behaviors were simulated in order to ana- lyze efficiency and power losses due to friction between the belt and the pulley halves. The results showed high loss of effi-

Fig. 7. Belt-pulley contact force distribution at no load for different clamping (axial) forces [39].

Fig. 8. Lumped-parameter discrete model of a metal V-belt CVT [53]. CICOCPriUae Plldsts, UIC SIUIL SPEC Is ALTIUS TIC ally Leal LU UIC lVUsalililiii UL Pulley Clallipilig LUCE Lally, Experiments done by a number of researchers, especially those done by Fujii and Kanehara, have shown that both tensile force in the band pack and compressive force in the belt elements aid in torque transmission. Their work suggests that these forces vary non-uniformly over the pulley wrap, which invalidates the assumption of constant band tension made by other researchers. Fujii et al. [51] experimentally investigated the tensile force in bands and the compressive force in belt elements. The authors observed the existence of active and idle arcs on the driving pulley and suggested that under some operating conditions, it is plausible for the bands to contribute up to 40-45% of the total transmitted torque. The authors also reported that the band tension distributions exist due to relative slip velocities between the bands and the segments. The band pack tensile force was observed to decrease around the smaller of the two pulleys in the direction of belt travel. Consequently, the band tension impeded torque transfer in high ratio and facilitated the transfer of torque in low ratio. This could happen to such an extent that in low ratio, for a range of lower torque levels, the entire torque load was observed to be transferred by the band tension alone. Fujii and co-workers [52] used a number of strain gauged belt elements to measure the forces acting in various directions on the belt element. The experiments were conducted on a constant transmission ratio CVT at low belt speeds and clamping pressures. Time histories of pulley normal force, belt compressive force, transmitting force (i.e. the tan- gential friction force between a belt element and the pulley), radial friction force, element shoulder force, etc. were recorded during the experiment. Since it was difficult to measure compression in the free span of the belt CVT system, a straight-line fit between the entry and exit conditions on each pulley was assumed. The pulley normal force was reported to be unevenly distributed on the contact arcs. The element normal force exhibited peaks at the entry and exit of the pulleys (similar to the observations of Ide and Tanaka [39]). Also, it was observed that the normal force per element was lower on the pulley with the larger wrap angle. The authors also reported the existence of idle and active arcs at higher transmission ratios. The radial friction force restricted the penetration of a belt element into the pulley, except at the pulley exits where it acted to retain the belt element in the pulley wedge. Fushimi et al. [53] developed a numerical model to calculate the steady-state force distributions and compared them to the previously published experimental results. The metal V-belt is modeled using a lumped-parameter approach with three kinds of springs and two kinds of interfacial elements for the block and the ring,

Fig. 9. Time histories of belt tensile and compressive forces at steady state for different speed ratios [55]. EEE EEE EEE EEE EEE EE EO Fujii and Kanehara also co-authored a paper with Fujimura [56,57] where they used mean coefficients of friction and shifting gradients to reveal the shifting mechanism of a metal V-belt CVT. Shifting gradient is a non-dimensional parameter which is defined as the radial increment of a block sitting on a pulley in radial direction per unit block path. The experimental results showed that thrust force and sliding speed influenced the mean coefficients of friction. The authors also observed that the shifting gradient was influenced neither by pulley speed nor by torque ratio. Torque ratio is defined as the ratio of trans- mitted torque to the maximum transmittable torque. An increase in shifting gradient was observed with increasing the thrust of a pulley in which the belt pitch radius was increasing, whereas it decreased with increasing the thrust of a pulley in which the belt pitch radius was decreasing. However, the authors did not include the influence of belt tensile and com- pressive forces on the shifting gradient of the variator. Fujii and Kanehara later co-authored a paper with Kataoka [58] where they analyzed shift mechanisms of the variator and characterized friction between the blocks and the pulleys. The authors concluded that the shifting gradient is governed not only by block elastic deformations (due to the pulley thrusts), but also by ring tension and block compression. The authors developed quasi-static equilibrium equations to estimate target pulley thrusts at steady and transitional states. Both driving and driven pulley thrusts were calculated by considering the forces on the blocks at pulley entrance. Experimental conformity was also reported for not only steady state, but also transitional state conditions. The frictional performance of CVT fluids and the frictional characteristics of block—pulley contact interfaces were evaluated by applying the mean coefficient of friction as a friction parameter. It was found from experiments that the esti- mated coefficient of friction of CVT fluids was not constant with respect to the operating conditions. It varied with the rel- ative sliding speed of the blocks with respect to the pulleys, sliding direction, and normal pressure acting on the V-surface of the block.

Fig. 10. Free-body diagrams of a metal V-belt CVT system: (a) band pack forces; (b) belt element forces; (c) forces on the pulley sheave; (d) torques on the driver pulley [61]. Srivastava and Haque [63] developed a metal pushing V-belt CVT model at steady state to study its microslip behavior and to define its operating regime. They discussed the influence of torques and axial forces on belt slip. Slip was based on the redistribution of elemental gaps and formation of inactive arcs (as proposed by Kobayashi et al. [34]). The model is able

Fig. 11. Time histories from belt CVT model: (a) element and pulley speeds; (b) element and band speeds; (c) belt element acceleration [61]

Fig. 12. Proposed self-excitation mechanism in a metal belt CVT [70].

Fig. 14. Free-body diagrams for chain CVT model: (a) forces on a chain link; (b) chain link interaction; (c) link-pulley contact description [16] Fig. 13. Time histories from chain CVT model: (a) pulley normal force: (b) chain tensile force [74].

Srnik and Pfeiffer [72-74] studied dynamic behavior of CVT chain drives for high torque applications. They developed a planar model of chain CVT with three-dimensional contact between a chain link and the pulley. Their work dealt with multi- body formalisms and finite element modeling. In order to account for the polygonal excitations due to the discrete nature of chain, the chain was modeled link by link. The chain links were thus modeled as kinematically decoupled rigid bodies inter- connected by spring-damper force elements, as proposed by Fritz and Pfeiffer [75]. The pulley was modeled as an elastic multibody system. Neglecting its own internal dynamics, the pair of rocker pins was modeled as one stiff massless spring acting along the direction of shaft axis. The model was simulated under the conditions of a constant driver pulley speed and a constant driven pulley load torque. Fig. 13 [74] depicts representative time histories from their chain CVT model. The authors used classical Coulomb friction law as well as its time efficient continuous approximation to calculate the con- tact forces between the links and the pulleys. The computational cost to determine the exact contact transitions between the

Fig. 15. Dynamic performance indices of chain CVT emphasizing reduced torque carrying capacity [16]. tigated the influence of clearance and friction on its dynamic performance indices for high torque applications. They reported that clearance and friction parameters drastically influence the performance of a CVT system, reduce its torque carrying capacity, and may even render the operation perilous by inducing chaos or irregular behavior in the system. However, their work did not include any mathematical analysis needed to capture such friction-induced chaos in a CVT system. Figs. 15 and 16 [16] illustrate representative time histories from the authors’ chain CVT model emphasizing the influence of contact-zone friction conditions and clearances between chain links on the torque capacity and dynamic performance of the chain CVT system (refer to [16] for details). The authors reported that certain contact-zone friction characteristics (e.g. a mix of lubri- cation and stiction) could cause reduction in the torque capacity of the CVT system and also induce hard-to-be-controlled irregular or chaotic dynamics in the system. Srivastava and Haque [60,61] developed a detailed transient dynamic model of a metal pushing V-belt CVT to evaluate its dynamic performance indices under the influence of pulley flexibility and vary- ing friction characteristics of the belt-pulley contact zone. Two different mathematical models of friction were incorporated to describe friction between the belt and the pulley. These friction models were able to capture effects due to kinetic friction, stiction, and lubricated contact conditions. The authors performed a comparative analysis on the dynamic performance of a metal V-belt CVT under the influence of these friction models. However, chaos related to friction-induced nonlinear phenom- ena was not reported in their work. Later, Srivastava and Haque [95] exploited their previous planar multibody chain CVT model [16] to identify interesting friction-induced chaotic phenomena in a chain CVT drive under high input-low load con-

Fig. 16. Dynamic performance indices of chain CVT: tensile force in links showing irregular periodicity [16].

Fig. 17. Typical operating characteristics of a CVT system [96]. An engine-CVT integrated control was suggested by Takiyama and Morita [97]. The authors developed an algorithm tc simultaneously control the vehicle velocity from the difference between the desired and actual velocity and the CVT ratic from the difference between the desired and actual engine speed. However, the authors neglected the transient character. istics of powertrain, which resulted in poor vehicle/powertrain performance during acceleration transients. Later, Takiyam< [98] modified the previous control algorithm [97] by incorporating air-fuel ratio effects in the model, and investigated the improvements in vehicle fuel economy. Vehicle speed control subsystem, fuel optimizing control subsystem, and A/F contro subsystem were treated as single input-output subsystems (SISO) independently using the decoupling control theory. Sak. aguchi et al. [99] developed an integrated engine-CVT control from the viewpoint of minimizing powertrain losses. The authors suggested that in an integrated engine-CVT control, it is necessary to consider powertrain losses in order to deter- mine the target engine torque. Yasuoka et al. [100] developed an integrated engine-CVT control algorithm to obtain the de. manded drive torque for optimum fuel economy. The authors used the engine torque to compensate for the drive torque response delay caused by the CVT response lag. The authors calculated the target torque by assuming that the accelerato! pedal travel represents the demanded drive torque and used the target gear ratio as the CVT ratio.

Fig. 18. CVT control strategies: (a) speed envelope strategy; (b) single track strategy; (c) off the beaten track strategy [108] Liu and Stefanopoulou [105] considered the wheel speed problem of an automotive powertrain with a conventional SI- engine directly connected to a continuously variable transmission. They developed a two-input two-output control structure

Fig. 19. System-level model for CVT ratio control.

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A wavelet-fuzzy logic based energy management strategy for a fuel cell/battery/ultra-capacitor hybrid vehicular power system

by Mehmet Uzunoglu

2009, Journal of Power Sources

Due to increasing concerns on environmental pollution and depleting fossil fuels, fuel cell (FC) vehicle technology has received considerable attention as an alternative to the conventional vehicular systems. However, a FC system combined... more

Fig. 1. Typical FC polarization curve. The ideal standard potential of a hydrogen/oxygen FC under standard state conditions (25°C and 1at.) is 1.229V with liquid water product. However, the actua FC potential is lower than the ideal potential value due to the irreversible voltage drops occurring in FC systems. There are three types of irreversible volt- age drops, namely activation overvo tage, ohmic overvoltage and concentration overvoltage. At low current densities, the activa- tion overvoltage is responsible for Besides, concentration overvoltage high current densities. The effects o corresponding variance of FC voltage the voltage drop of the FC. becomes more significant at f these voltage drops and the can be seen from the FC polar- ization curve in Fig. 1. The most efficient operating region for FC is the linear region corresponding to cell voltage between 0.55 V and

layer capacitor effect. The FC equivalent electrical circuit consider- ing the charge double layer capacitor is shown in Fig. 2 [17]. This electrical capacitor represents the layer of charge on or near the electrode-electrolyte interface, which is a store of electrical charge and energy [18]. Considering the effects of this double layer capac- itor effect, the dynamics of the FC output voltage can be obtained more accurately.

Fig. 4. Battery electrical equivalent circuit [20]. The natural structure of UC is appropriate to meet transient and instantaneous peak power demands. The UC bank is used to pro- vide the difference between the load demand and the FC system

Fig. 3. Dynamic model of the FC system. is strongly dependent on battery SOC, it can be calculated as [20]

output power. Fig. 5 shows the electrical equivalent circuit used in the UC model development. The developed model is verified by experimental studies using a Maxwell® 430 F, 16 V UC, which is cur- rently operational in the authors’ lab. Detailed description about the modeling of the UC system can be found in [21], which is a previous study of the authors.

Fig. 6. Power demand of a vehicle according to UDDS cycle. In order to evaluate the dynamic response of a developed methodology and make suggestions through the development in

Fig. 7. A schematic diagram of the developed energy management strategy.

Fig. 9. Fuzzy logic controller rule base.

Fig. 11. The variation of FC system dc output power.

Fig. 12. The variation of battery pack charging and discharging power.

Fig. 13. The variation of UC bank charging and discharging power.

Fig. 16. The variation of state-of-charge of the battery pack.

Fig. 14. Total output power of the hybrid system.

Fig. 19. The variation of the amount of hydrogen flow required to meet the loac change.

Fig. 17. The variation of state-of-charge of the UC bank.

In this study, a lithium-ion battery pack is utilized both for sup- plying a portion of the base load together with FC and capturing the braking energy together with UC. In this section, the dynamic model of the lithium-ion battery is introduced. The lithium-ion battery model parameters used in proposed model are as follows: The battery output voltage can be calculated due to the battery open circuit voltage and voltage drop resulting from the battery equivalent internal impedance. Accordingly, the battery output voltage may be expressed as

Specific characteristics of UDDS cycle. Table 1

Maxwell Boostcap BMOD0430 P016 ultra-capacitor specifica- tions [33].

Panasonic NCR18650E lithium-ion battery cell specifications [32].

Weight and volume comparison of the developed FC/Battery/UC hybrid system with FC/UC hybrid structure. nominal voltage of 16 V with a capacitance of 430 F. Since we have an experimentally validated simulation model for Maxwell Boostcap® BMOD0430 UC, this module is selected in this study. The UC bank consists of 2 parallel connected strings. Each string includes 5 UC units connected in series. The UC bank must be capable of supply- ing the maximum power value of the UC reference power signal (28 kW) in Fig. 8 for 10s. Thus, the proposed UC bank design must have a minimum rating of (28000/0.75) x (10/3600) = 103.7 Wh. The proposed UC unit has a capacitance of 430 x (2/5)=172 F and a voltage of 16 x 2=80V, corresponding to 152.88 Wh of energy. Thus, this UC bank design can easily meet the required amount of energy. To conclude, it can be seen that the overall hybrid system design can satisfy the requirements of UDDS cycle as well as the developed energy management strategy. Table 5

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A Multi Objective Fuzzy-Based Controller for Front Differential Vehicles by Electrical Traction System on Non-Driven Wheels

by Reza Hoseinnezhad

2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing

high speed respectively by proper fuzzy controller. Finally, a series of MATLAB/SIMULINK simulation will carried out to evaluate the performance of the proposed structure.

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Fuel economy of hydrogen fuel cell vehicles

by Aymeric Rousseau

2004, Journal of Power Sources

On the basis of on-road energy consumption, fuel economy (FE) of hydrogen fuel cell light-duty vehicles is projected to be 2.5-2.7 times the fuel economy of the conventional gasoline internal combustion engine vehicles (ICEV) on the same... more

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Plug-In Hybrid Electric Vehicle Value Proposition Study: Phase 1, Task 2: Select Value Propositions/Business Model for Further Study

by karen Sikes

2008

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Energy efficiency technologies for road vehicles

by Steven Plotkin

2009, Energy Efficiency

A key message of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change is that improved energy efficiency is one of society's most important instruments for combating climate change. This article reviews a... more

Fig. 1 World transport energy use in 2000, by mode (WBSCD 2004) Transport activity is expected to grow robust over the next several decades. A key driving force is the rapidly growing economies of the developin world, led by China and India, and the extraordi- nary growth in ownership of private vehicles in these areas. Also, energy-intensive intercity and international travel is growing rapidly and freight transport is expanding even faster than overall passenger travel. Unless there is a major shift away from current patterns of energy use, this expansion of travel activity will cause total trans- port energy use and carbon emissions to rise by about 80% above current levels by 2030 (WBSCD 2004). o< tic)

Fig. 2 Sales-weighted fuel economy of new cars sold in US, Japan, France and Germany. Data source: EPA (2008) and EDMC (2007) The rise of the market for light trucks played a significant role in the US stagnation in fuel economy between 1987 and the present. The SUV mar! of light trucks increased to about 50%, wi efficiency improvement. ket share increased from less than 10% of the overall new light- duty vehicle market in 1990 to about 30% of vehicles built each year since 2003. The overall market share h trucks averaging 5—7 mpg lower than cars. This trend was backed by low fuel prices; in the future, higher fuel prices and stricter regulation should push fuel

ative braking system that uses the vehicle’s electric motor as a generator to capture braking energy and to ances aoa Blea: Picadas:

Fig. 4 Price increase due to the new technology for various gasoline (G) and diesel (D) vehicles as a function of efficiency improvement. Baseline is the current gasoline vehicle data Source: EUCAR/Concawe/JRC (2006), MIT-Bandivadekar et al. (2008), IEA (2008) we can get some implication for the market. We assume the typical situation in US that a vehicle has the fuel efficiency of 10 km/L (24 mpg) and travels 19,000 km (12,000 miles) yearly, resulting in annual fuel consumption of 1,900 L (500 gal). If fuel cost is $3/galon (80 cents/L), fuel cost amounts to $1,500. If the fuel efficiency improvement is 50%, customers need 12 years to pay off the price increment. In Japan, where fuel price is almost twice as high, but distance traveled per year is around 12,000 km, only 7 years are needed to pay off it. Although this does not mean necessarily that customers will be willing to buy the vehicle with such an increased price, this fuel savings/ vehicle cost tradeoff may commercially allowable if future consumers are highly concerned about fuel economy.

Fig. 5 Comparison of fuel economy between on-road value for stocks and test value for new cars in Japan and US

Table 1 Increase of fuel efficiency due to various vehicle technologies Source of data: EPA HP (http:/Avww.fueleconomy.gov/feg/atv.shtml); Duleep 2008, HM Treasury 2007, IEA 2008

‘References (1) GM/LBST 2002; (2) GM/ANL 2005; (3) EUCAR/CONCAWE/JRC 2006; (4) JHFC 2006; (5) Heywood and Weis: 2003; (6) Bodek and Heywood 2008; (7) Cheah et al. 2007; (8) Kromer and Heywood 2008; (9) IEA 2008 Table 2 Projections of vehicle efficiency improvement relative to the base gasoline vehicle (%) So far we have only considered the fuel efficiency improvement due to drivetrain and load reduction technologies. As already mentioned, there is a gap between the fuel economy shown on new vehicle labels and that achieved by consumers on road. Typical examples are shown in Fig. 5. Surprisingly on-road fuel economy data for Japan and US are very similar while those of new cars are quite different. Although on-road fuel efficiency data are estimated data and have some inherent uncertainties (Schipper 2007), the difference between Japan and US could be interpreted in terms of the different driving condi- tions, such as average speed of vehicles and driving distance. Within the major cities in Japan, the average travelling speed of vehicles is less than 20 km/h, while the national average is around 37 km/h (MLIT data'”). This can be compared with the average speed of 32 km/h (20 m/h) for US urban driving test mode. It is well known that the vehicle speed strongly affect the fuel consumption. The increase of average speed by 10 km/h could reduce fuel consumption by more than 15-20%'?. As can be seen above, it is very important to improve traffic flow, especially in urban areas, to improvement due to drivetrain and load reduction

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Tire Pressure Monitoring [Applications of Control

by Levent okan

2007, IEEE Control Systems Magazine

FIGURE 1 A direct tire-pressure monitoring system. Sensors in each tire measure pressure. The data are transmitted to a receiver locat- ed on the dashboard, which indicates loss of pressure. TPMSs are classified into two categories, namely, direct and indirect. In direct TPMSs, the pressure drop is calcu- lated based on actual pressure measurements through sensors. In contrast, measurements such as wheel speed are used in indirect TPMSs. A direct TPMS can inform the

vehicle is parked, the sensor transmits the signals once an hour, while other models are equipped with speed detec- tors that shut down the transmission. More sophisticated sensor modules contain a low-frequency receiver integrat- ed circuit, which waits in standby mode and wakes up the sensor once it detects a trigger FIGURE 2 (a) Sensor module and (b) its fitting. The mass of the sensor is minimized to reduce the effect of centrifugal force due to rotation of the tire. The pressure sensor is mounted on the tire rim. The valve stem acts as an antenna for the built-in transmitter. (Printed with the permission of Hella, Inc.) f= hE Vek SARI SS SE Ne. aa =. ee an antenna and control unit in Of centrifugal force due tc which data are received and Sim Seises an arignna transferred to a _ central processor to trigger the warning system. In advanced models, the receiver module is portable with a built- in processor, display unit, and warning system, which can be placed within the driver’s reach. This unit is powered by either its own battery or the car battery. The receiver is programmed by the manufac- turer to associate each sensor with a corresponding tire and needs to be reprogrammed when a tire or sensor is replaced. The receiver can be integrated with the vehicle remote keyless entry system through proper design of the transmission frequency and pro- tocols. The basic portable receiver display unit has color symbols with an LED specific to each tire to identify the underinflated tire as shown in Figure 4(a), while advanced models can display icons and tire pressure as shown in Figure 4(b). A challenging task of the receiver is to avoid data interference from nearby vehicles. where v is the vehicle’s speed, r is the tire’s nominal radius, 7, is the tire’s effective radius, and 6; is the tire

FIGURE 4 Receiver with display. Display technology ranges from (a) portable units with LEDs to indicate tire pressure loss to (6) advanced built-in units that display tire pressure. (Printed with the permission of SmarTire, Inc.) FIGURE 3 Pressure-on-demand sensor module circuitry. This module includes a transmitter, wakeup unit, microcontroller, and sensor. As long as the vehicle is stationary, the tire pressure sensor stays in sleep mode.

FIGURE 6 Hybrid tire pressure monitoring system configuration. A hybrid TPMS, which is a combination of a direct TPMS and an indirec' TPMS, uses fewer tire pressure sensor modules than a direct TPMS.

FIGURE 5 Tire and vehicle speeds. Tire pressure changes result in changes in tire rotational speed. Tire rotational speeds are mea- sured in all vehicles by the antilock braking system.

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Overview of boosting options for future downsized engines

by Ricardo Martinez-botas

2011, Science China Technological Sciences

Driven by a demand for better fuel economy and increasingly stringent emissions regulations over a wide range of customers and applications, engine manufacturers have turned towards engine downsizing as the most potent enabler to meet... more

Figure 1 Fuel consumption and CO; emissions projections for both gasoline and diesel engines. Diesel engines are projected to move up to 60% efficiency in 2014 compared to 38% in 2004, through waste heat utili- sation, of which various super/turbocharger and power tur- bine combinations will play a central part in the downsizing The main, general OEM needs for both gasoline and diesel engines are the ability of the boosting system to provide high BMEP, EGR rates and efficiency throughout the oper- ating range, reduced and/or variable back pressure, rapid transient response, altitude performance, downsizing,

Figure 2 Advanced downsizing trend for gasoline engines. In this section the main, key technologies in use today or

Figure 3 Variable Geometry Turbocharger turbine with pivoting nozzle mechanism in (a) closed positions at low engine powers for high exhaust energy recovery and (b) at open position when the engine power and ex- haust flow are sufficient [6].

Figure 5 (a) Twin entry and (b) double entry turbine inlet casing.

Figure 6 Potential electrical turbomachinery layouts [4].

Figure 7 Required pressure ratio at different BMEP for 2/4 stroke en- gines. 3.2.1 Single Stage High Pressure Ratio Compressor Wide compressor operating ranges are a challenge for

Figure 8 Two types of topologies casing treatment. The multistage compressor (usually two-stage) which is

Figure 11 Different types of multistage compressors [14, 15]. (a) Back to Nose; (b) Back to Back; (c) Axial-Radial. connected on a common shaft with a turbine has been de- veloped to achieve high boost pressure while keeping low rotational speed which is beneficial in terms of stress reduc- tion and efficiency as well as operating range. Figure 11 shows three types of two-stage compressors, which are the back to nose, back to back two-stage centrifugal compressor arrangements and the axial-radial compressor. The first two types can achieve higher boost pressure than the third type

Figure 10 Measured performance comparison of compressors with/ without SRCT. Figure 9 Averaged static pressure along the meridional length of pas- sage.

Although the use of turbocompounding is relatively new for

Figure 13 Series sequential turbocharger installation [4].

Figure 17 Electric (Hybrid) Supercharger [20] FREE US arse METER ARpate vaereaee ene Be [ne (1) TurboSuperGenerator. The turbosupergenerator (TSG) combines supercharging and hybridisation advan- tages with the additional benefits of multi-stage turbo- charging. It allows electrical supercharging as well as elec-

In this section boosting systems under development are presented. These are at least relatively new with minimal or non-existent market uptake yet.

Figure 16 Electric (Hybrid) Turbocharger [19] The electric (hybrid) supercharger is avery similar device to

Figure 15 (a) Typical mechanical turbocompounding installation and (b) power surplus chart which is capitalised upon by the turbocompound installation, Hopmann, 2002 [16]

Figure 18 Electric (Hybrid) Supercharger [21]

Figure 20 Electric Turbocompound installation [24].

Figure 19 Mechanical SuperTurbocharger [22].

Figure 22 Gasoline and Diesel turbocharged engine specific torque and specific power performance with boundary of single to two-stage cross over fo: gasoline [26]. The level of boosting achievable by available and under- Depending on application the slope of the development It is obvious from Figure 22 that two-stage turbocharger systems are more capable in both peak, specific torque and peak, specific power than any combination of single stage turbocharger, regardless of cam phasing, injection system or number of turbine entries (scrolls). Another conclusion from Figure 22 is that gasoline engines even with simpler boost- ing systems offer a higher specific power output thus en- hancing the statement in the introduction of this paper about the greater scope for downsizing offered by gasoline en- gines. In addition, in terms of comparative bases, the range of improvement of current and future boosting systems spans from the category “Base Engines” to the category “Gasoline 2-Stage” for the present gasoline envelope and from the category “Diesel VGT” to the category “Diesel 2-Stage” for diesel engines.

Figure 23. Bmep achievable with BorgWarner series sequential turbo- charger, VGT and a standard fixed geometry turbocharger with a wastegate (WG) [26].

Figure 25 Increase in thermal efficiency of under-development boosting systems compared to the same N/A baseline as in Figure 24. Figure 24 Increase in thermal efficiency of available boosting systems compared to N/A baseline.

Here, the situation is not as clear except to say that many of the under-development systems offer similar levels of

Figure 26 Increase in packaging requirement due to available boosting system installation compared to N/A baseline.

Figure 27 Increase in packaging requirement due to under-development boosting system installation compared to N/A baseline of Figure 26.

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Plug-In Hybrid Electric Vehicle Value Proposition Study: Interim Report: Phase I Scenario Evaluation

by karen Sikes

2009

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An Advanced Engine Thermal Management System: Nonlinear Control and Test

by John Wagner

2005, IEEE/ASME Transactions on Mechatronics

Internal combustion engine thermal management system functionality can be enhanced through the introduction of smart thermostat valves and variable speed electric pumps and fans. The traditional automotive cooling system components... more

Fig. 1. Simplified automotive thermal system.

Fig. 2. Scale experimental cooling system test platform. pump is dependent on the fluid density, shaft speed, system ge- ometry, and pump configuration so that to a worm gear assembly which connects to the multiposi- tion valve. The governing equation for the armature current, day(t) € KR, may be described as

Fig. 3. (a) Temperature response for highway operation (Case 1-Test 1A) Tsp = 60°C, Tp = 1.75 °C, T = 1508, Qin = 8.0 kW (b) Temperature tracking error for highway operation (Case 1-Test 1A) T;, = 60°C, To = 1.75°C, T = 1508, Qin = 8.0 kW. (c) Percentage valve opening for highway operation (Case 1-Test 1A) T,, = 60°C, Tp = 1.75 °C, T = 150s, Qin = 8.0 kW.

Fig. 4. (a) Temperature response for highway operation (Case 1 — Test 1B) Tsp = 66°C, To = 2.0°C, T = 908, Qin = 11.0 kW. (b) Temperature tracking error for highway operation (Case 1-Test 1B) T;, = 66°C, Ty = 2.0°C, T = 90s, Qin = 11.0 kW. (c) Percentage valve opening for highway operation (Case 1 — Test 1B) T;, = 66°C, To = 2.0°C, T = 908, Qin = 11.0 kw.

Fig. 7. Transient temperature response of engine and radiator nodes to the valve’s motion between 10% and 90%.

Fig. 6. Temperature response for quick heat scenario (Case 3) with 8.0 kW input heat and auxiliary 4.0 kW to supplement engine heating until set point temperature achieved.

Bo) Vi) ye Clemson, SC, where he currently holds the position of McQueen Quattlebaum Professor. His research in- terests are nonlinear control techniques for mechatronic applications such as electric machinery, robotic systems, aerospace systems, acoustic noise, under- actuated systems, magnetic bearings, mechanical friction, paper handling/textile machines, flexible beams/robots/rotors, cable structures, and vision-based sys- tems. He also focuses on the development of real-time hardware and software systems for control implementation.

* Power consumed by the smart valve is neglected; water pump and radiator fan are contributors. COMPARISON OF FULL-POWER CONTROLLERS AND VALVE

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Partially premixed charge compression ignition engine with on-board production by exhaust gas fuel reforming of diesel and biodiesel

by Thanos Megaritis

2005, International Journal of Hydrogen Energy

The application of the exhaust gas fuel reforming process in diesel engines has been studied experimentally as a way to assist the premixed charge compression ignition operation by substituting part of the main fuel with hydrogen-rich... more

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Mechatronic design and control of hybrid electric vehicles

by Bradley Glenn

2000, IEEE/ASME Transactions on Mechatronics

The work in this paper presents techniques for design, development, and control of hybrid electric vehicles (HEV's). Toward these ends, four issues are explored. First, the development of HEV's is presented. This synopsis includes a novel... more

Fig. 1. DOH for HEV. the designer with quantifiable justification for focusing most of his/her energy on using the EM to maximize ICE performance. A 10% increase in performance of a 66-kW ICE will eclipse the same percentage increase in performance of a 20-kW EM. This strategy does not tell the designer to neglect the EM, but it does offer a quantifiable metric that the designer can use to determine where control efforts should be emphasized. This highlights the “synergy” principle of mechatronic systems because it helps ensure that the control strategy can actually take advantage of the vehicle configuration. In addition, when cost concerns are considered, the DOH can be used as part of a cost-benefit analysis.

Fig. 2. Efficiency map with operating points.

alti i ee lS QUALITATIVE DESCRIPTION OF A LOAD-LEVELING STRATEGY FOR A PARALLEL HEV.

Fig. 4. Generic efficiency map of an ICE, showing the locations of arbitrary operating points relative to the point of best efficiency.

Fig. 5. Simple FLC system with two inputs and one output. other words, one is 75% certain that the torque value of 0.875 is high positive. SOC = 0.50 is a member of only one MSF, medium. An SOC of 0.5 is considered to be 100% “medium.” Because of how the MSF’s have been chosen, the degrees of membership for each input must add up to 100%. However, it is possible to define MSF’s of qualitatively and quantitatively different shapes. to 0.25. The larger triangle on the right has the same values as its corresponding T (Tyqz positive), but its height has been reduced to 0.75.

Fig. 6. Fuzzification, inference, and defuzzification procedures for the presented example (system with two inputs, one output, and two rules).

Fig. 8. VCU with inputs and outputs in the overall system. Fig. 7. Driver-vehicle feedback control systems.

generator. Provided with this information, the VCU fits into the overall system as shown in Fig. 8. — cient, coefficient of rolling resistance, wheel radius, gear ratios, and total vehicle mass. It employs the standard power equations as used in vehicle dynamics. At a given speed, the power equa- tion relates to load torque T,..,4 that can be computed according ;

Fig. 10. Efficiency map for HEV using the efficiency strategy.

Fig. 12. Efficiency map for HEV using the fuel-use strategy.

to the overall drivetrain is limited by the state of charge (SOC) of the battery pack. input signal to the engine control unit (ECU). The ECU then controls the amount of fuel injected into the cylinders. By mod- ifying the input signal to the ECU, the torque production of the ICE can be changed.

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Plug-in Hybrid Electric Vehicle Value Proposition Study - Final Report

by karen Sikes

2010

Figure 19: California summer load duration curves with and without PHEV added demand

Figure 20: California generating capacity, initial generation amounts, and added generation from PHEVs.

Figure 21: Load Duration Curves for ECAR loads.

Figure 23: ECAR summer load duration curves with and without PHEV added demand

Figure K - 15: California summer load duration curves with and without PHEV-added demand Dispatch Results

ECAR’s electricity generation mix is dominated by coal, which provided insight on how GHG emissions originating from PHEVs in this region compared to GHG emissions originating in a natural gas-dominated generation mix like in southern California. Blended electricity rates would account for peak versus non- peak rates throughout the region. PHEVs are forecasted to comprise just more than 1 million of the region’s private vehicle fleet in 2030.

Commercial Building Owners. For commercial vehicle-to-building (V2B) concepts to be worthwhile for the building owner, the net electricity savings will need to outweigh the monthly operating costs needed to support this program, including some form of compensation to participating vehicle owners whose battery packs are exposed to added stress. The vehicle owners will expect some form of compensation, either monetary rebates or non-monetary incentives (e.g., preferred parking spaces), in exchange for wear and tear on the battery. The net savings to the building will need to be sufficient to justify the capital and Ongoing operations cost for the program.

Figure 2: Network of data flow used in the regional case study analyses.

Figure 5: Breakdown of component costs (left) and schematic of powertrain (right) for an ICE The basic architecture of the ICE is the least complex of the mid-size sedans analyzed in this case study, comprised of only the glider (vehicle minus the powertrain), engine, and transmission. (A motor/inverter, energy storage, and recharging plug/charger are not included in an ICE.) As shown in the cost breakdown in Figure 5, the estimated purchase cost for this ICE in 2030 is approximately $21,400 (using the cost equations in Table 4). A simple schematic of an ICE powertrain is also provided below. See Appendix D for detailed vehicle purchase cost calculations.

Figure 6: Breakdown of component costs (left) and schematic of powertrain (right) for HEV.

hile maintai han seen in modeling pur in this study. oattery pack PHEVs are di at home through the electric utility grid. To accommodate the increased dependence on electric power WV h a larger capacity ning an poses fferenti ated from HEVs primarily by their ability to charge with off-board electrical energy appropriate vehicle weight, the PHEV uses a battery pack wit 0 maintain a consistent performance level among the three ve oane ectrical socket for recharging. In this study, a pre-transmission he HEV. Similar to this study’s HEV, an engine of reduced size was used in the P hicle types si An inverter-integrated charging plug and charger are needed to connect the e , paralle powertrain architecture was used for the PHEV. As shown in the cost breakdown in Figure estimated purchase cost for this PHEV in 2030 is $26,925 (using the cost equation simple schematic of the PHEV parallel powertrain analyzed in this case study is sh s in Tab Appendix D for detailed vehicle purchase cost calculations. 7, th HEV fot mulatec nhanced hybrid e e 4). A own below. See Figure 7: Breakdown of component costs (left) and schematic of pre-transmission parallel powertrain (right) for PHEV.

Figure 8: Initial vehicle purchase cost comparison for ICEs, HEVs, and PHEVs produced in both 2010 and 203¢

Figure 9: Overall vehicle purchase cost with state sales tax and present value of money includea To account for common financing options and time-of-purchase fees, Figure 9 displays the overall vehicle purchase cost differences between each vehicle type in 2030 once state sales taxes and present dollar values are incorporated. The state sales tax rates for California and Ohio are assumed to be 8.25% and 5.5%, respectively. In 2030, these rates are estimated to rise steadily to 10% and 6%, respectively. For the purposes of this study, all vehicles are financed over five years at a 6% interest rate with no down payment.

Figure 10: Total fuel (liquid and electricity) costs over a ten year vehicle lifetime in southern California.

Figure 11: Total fuel (liquid and electricity) costs over a ten year vehicle lifetime in ECAR / Cleveland, Ohio The average annual amount of electricity used by a single PHEV-30 in ECAR/Cleveland, Ohio, was simulated in PSAT to be approximately 2,800 kilowatt hours (identical to southern California), adding an estimated $250 to the PHEV-30 yearly operating cost, or $2,500 to the lifetime operating cost. This additional cost still results in significant fuel savings during the lifetime of the vehicle compared to ICEs and HEVs (see Figure 11). The lifetine combined fuel (liquid and electric) costs for the PHEV-30 amount to roughly $6,750, which offers an operating cost savings of nearly $10,200 and $5,000 relative to ICEs and HEVs, respectively, in the 10-year period 4.1.2.2. Maintenance Costs It has been speculated that PHEVs will have lower scheduled maintenance costs relative to ICEs and HEVs for several reasons. First, PHEV engines are running for a lower percentage of the vehicle’s operating time, meaning they may have longer intervals between oil changes and air filter replacements. Second, regenerative braking on HEVs and PHEVs reduces wearing on the brakes and the need for brake replacements. These costs contribute significantly to a vehicle’s overall operating costs in its lifetime. As previously mentioned, the PHEVs analyzed in this study have a parallel hybrid powertrain. It should be noted that some series hybrid powertrains do not require transmissions, which could further reduce the cost to maintain a PHEVs (e.g., fluid changes). All-electric vehicles, such as

Figure 13: Overall vehicle operating cost comparison for ICEs, HEVs, and PHEVs in 2030 over a ten year lifetime in each case study location.

Figure 14: Total vehicle operating costs for each vehicle type once present value of money is incorporated. Total Vehicle Operating Cost over 10 Years

Figure 15: Outcome of building owners squaring off each day’s peak by purchasing extra power in the morning to completely recharge the batteries.

Figure 17: Load Duration Curves for California loads before and after imports California’s marke ORCED, the total i import amounts were then applied to each hour based on the load in that hour as com average load for th at peak demand, imports would only be half of the amount of th imports at the mini e season. Rather than a constant amount of import each hour, it was assumed tha € average demand. Si ve demands. Th pared to the milarly, the mum demand were only 75% of the amount at the average demand in each season. This represents typical market behavior where market trading often peaks during the i demand periods. At peak times, most regions are trying to mee demands, most regions have a surplus of low-cost powe California’s imported power have reduced the demands that ge nerators experience. their own demands, while at minimum r. Figure 17 shows the LDCs before and after ntermediate shows a large amount of imports to meet demand. To simulate the net imports into mports were divided among the three seasons based on their relati e 4.3.1.3. PHEV Demand

Figure 24: Net demand during peak week if all PHEV owners plug in immediately following afternoon work commute at 220V. If smart charging pract ices are not well established by 2030 and customers have no incentive to charge overnight, then PHEV charging could potentially have a negative effect on the grid’s peak load. For example, Figure 24 de following work, from 5 monstrates that if all PHEV owners chose to plug in their vehicles immediately p.m. - 6 p.m. on weekdays at 220V, then peak system demand could increase by 4,500 MW (from 114.9 GW to 119.4 GW). Offering incentives such as time-of-use pricing could encourage customers 0 shift to charging in the late evening and early morning hours.

Figure 26: Regional case study comparisons of CO2 emissions between ICEs, HEVs, and PHEV-30s in 2030. W2W CO, Emissions for Each Vehicle Type

Figure 27: Regional case study comparisons of CO2 emissions between ICEs, HEVs, and PHEV-30s in 203 Total W2W Energy Used for Each Vehicle Type 4.4.3. Increased Renewable Generation

Figure 28: PHEV-30 energy storage utilization during ten year (150,000 mile/ operation) Li-lon Battery Energy Utilization vs. Time*

Li-lon Battery Energy Utilization vs. Time* Figure 29: Potential battery pack conceptual design with only one half of the original reserve storage capacity to allow reduced battery capacity

Figure 30: Potential battery pack conceptual design without reserve capacity to accommodate cycling and aging losses

As previously mentioned, the majority of a PHEV’s operating costs savings originate from supplementing liquid fuel use with less expensive electricity. A variety of AERs were investigated in this study to see if adding larger battery packs, resulting in more electricity use, always translated to lower total ownership costs. Figure 31 indicates that total cost of ownership appears to decrease as the AER increases. The figure indicates that PHEV-30s and PHEV-40s in the ECAR region in 2030 actual have a lower total ownership cost compared to a comparable HEV in the same region, mainly because of ECAR’s low electricity prices. In the southern California region, PHEVs do not indicate a cost advantage compared to HEVs at any AER, given the assumptions taken in this study. However, PHEVs are still considered cost competitive with HEVs. In no cases investigated in this study do PHEVs have a higher cost of ownership than comparable ICEs in 2030, even though ICEs are the least expensive to purchase.

Figure 33 displays the W2W energy use for an ICE, HEV, and PHEVs with AERs of 10 miles to 40 miles in 2030 assuming a 70/30 split in E10 and E85 use. For comparison, today’s conventional ICE operating 100% on reformulated gasoline is shown. It can be concluded that ICEs easily use more energy per mile than any of the other vehicles in the study. This is expected, since the ICE has no ability to regenerate energy from braking activities, so its efficiency would be lower than that of comparable hybrids. This figure also shows the impact that vehicles with larger battery packs have on energy use. As battery size (and, Figure 32 provides a basic comparison of CO2 emissions between the three vehicle types operating in both regions in 2030, assuming a 70/30 split in E10 and E85 use in 2030. For comparison, today’s conventional CE operating completely on reformulated gasoline is shown. Many improvements have been made to the fuel economy of all three vehicle types between now and 2030, primarily because of a reduction in glider . These CQ2 emissions include those from production and use of the fuel and energy sources, but hey do not account for emissions from the manufacturing of the vehicle or its components. For the southern California region, which uses natural gas for most of its margin generation, the PHEVs investigated in this study have less W2W emissions than ICEs. However, in ECAR, where coal is used for well over half of its margin generation mix, PHEVs with an AER above 20 miles have a slightly higher production of W2W COz emission than comparable ICEs. The use of the HEV shows significantly lower production of CO2 when compared to any other vehicle in the study in either region. A breakdown of CO2 emissions for each of these cases, showing emissions originated from feedstock, fuel, and vehicle operation, can be found in Appendix G.

Figure 34: Imported oil displaced over lifetime of vehicle relative to an ICE, assuming a 70/30 split of E10 and E85 use in 2030

Figure 35: Increase in ECAR grid load due to PHEV fleets with varying AERs

Figure 38: Effects of vehicle glider weight on total W2W energy used

Figure 39: Effects of varying electricity cost of total lifetime ownership cost

Figure 40: Imported oil displaced over lifetime of vehicle relative to an ICE with the same altered drive cycles. A 70/30 split of E10 and E85 use is assumed in 2030 is assumed. The addition of 2.5% grades to portions of the base case drive cycles was investigated to see how fuel economy and lifetime fuel and electricity costs were. No hills, uphill or downhill, were included in the regional case studies. Overall, modeling results indicated negligible effects on fuel economy, even when the vehicle glider weight was increased and reduced. These calculations can be found in Appendix E.

Figure 41: Effects of varying fuel blends on CO2 emissions When the upstream energy factors for the production of biofuels are taken into account, the energy advantage still points to high concentration petroleum fuel in an efficient hybrid configuration, as shown in Figure 42. Based solely on W2W energy calculations, the HEV-E10 has a clear advantage. In the Well-to-Tank portion of the fuel cycle analysis, petroleum-based fuels have among the lowest energy losses while cellulosic ethanol has among the highest energy losses. Energy consumption in the Tank-to- Wheel portion of the fuel cycle analysis was also lower for petroleum-based vehicles than vehicles operating mostly on ethanol. However, it should be noted that mostly renewable energy is consumed in vehicles operating primarily on cellulosic ethanol. A breakdown of energy used for each of these cases, showing whether energy was used in during feedstock preparation, fuel, or vehicle operation, can be found in Appendix G.

Figure 42: Effects of varying fuel blends on total W2W energy used

Figure 43: Imported oil displaced over lifetime of vehicle relative to an ICE operating on E1¢

The effect of carbon tax may have a counter-intuitive effect on W2W CO emissions when vehicles are charged off peak, as is shown in Figure 45. The generation mix in the ECAR region generally has a high percentage of coal-generated electricity. In the case of a high carbon tax rate ($191/ton), utilities would likely change their generation mix to shift some of the coal generation from the base mix to the margin. Since the PHEVs in this study are presumed to be charged from the margin mix, the grams CO2 per mile increases relative to the base case. It is assumed in this scenario that utilities would be able to change the operating strategy of the coal-fired power generation plants to meet the margin operating strategy.

Figure 45: Effects of carbon tax rates on ECAR COz emissions

Perhaps one of the most interesting portions of this sensitivity study is the impact power generation mixes selected by regional utilities can have on W2W CO> emissi ons. More states are adopting standards that require the increased use of low-carbon or carbon-free energy sources in their mix to help reduce the carbon footprint and improve local air quality. Such methods include more utilization of nuclear power and non-carbon renewable energy resources (e.g., hydro, geot hermal, wind, solar). W2W CO2 emissions can be further reduced when coupled with a low-carbon fuel, such as E85, used to power a region’s plug-in electric vehicles. Figure 47 compares all nuclear energy, plus E85 to the projected base for both southern California all renewable energy, and all renewable energy and ECAR generation mixes in 2030. The coal-heavy ECAR region could make a change in the power generation mix to more environmentally friendly energy sources, as shown in the figure, which would reduce W2W COz emissions for transportation electricity by more than 50%. When combined with an average E85 blend, both the environmental and petroleum displacement portions of the transportation energy equation are satisfied, leaving only the cost and market availability as variables. A breakdown of CO2 emissions for each of these cases, showing whether emissions originated from feedstock, fuel, or vehicle operation, can be found in Appendix G.

The impacts of capturing and sequestering the coal on each region’s total W2W use were investigated in this study. To equip and operate a coal plant with CCS, a considerable amount of addition fuel and energy is required. Because GREET does not currently have an option for modeling CCS, estimates from the Intergovernmental Panel on Climate Change (IPCC 2005) are referenced. The panel estimates that a new supercritical, pulverized coal that uses current technology would need an additional 24%-40% of energy. Energy use associated with mining and extraction of coal would also rise. If the percentage increase was added to the ECAR region's project generation mix in 2030, total energy used in the ECAR region could rise to 4,800Btu-5,400 Btu per mile.

Figure 49: Impacts of varying market penetration rates of PHEV-30s on ECAR’s grid load

Effects of Varying Liquid Fuel Prices on Total Lifetime Ownership Cost

Figure 51: Effects of varying electricity costs on lifetime ownership cost

Public policymakers benefit from the ability to meet balance of trade, GHG emissions, and national securit goals. The most pertinent risk factor for the public sector was the potential loss of opportunities for vehicle and battery manufacturing to foreign manufacturers. Figure 53: Summary map of OEMs’ perceived risks and benefits associated with the emerging plug-in electric vehicle market

Figure 54: Summary of total cost of ownership for each vehicle type, once present value of money is applied. regions. Carbon taxes would account for a small percentage of a vehicle’s operating costs, so ebbs and flows in those rates typically do not have a strong impact. Finally, the projected cost of PHEV components (e.g., energy storage) accounts for a significant portion of the purchase price, so shortfalls in long-term cost goals may result in less financially appealing PHEVs — especially in those with high AERs and larger battery packs.

Figure 55: Net demand during peak week if all PHEV owners plug in immediately following afternoon work commute at 220V. Utilities. From a utility's perspective, the relatively slow market penetration of PHEVs assumed in this stu in combi peak de nation with smart c practices are not well estab peak de that if al 220V, th this, measures should be in place, such as time clocks with pricing or other incentives, to encourage customers to shift mands could poten en peak system de harging that shifts demand to off lished by 2030, and customers h ~peak times leads to very little impact on over mands, while providing the utility with additional sales during off-peak times. If smart charging ave no incentive to charge at nighttime, then ially have a negative effect on the grid. For example, Figure 55 demonstrates PHEV owners chose to plug in their vehicles immediately following work, from 5 p.m.-6 p.m., at mand could increase by 4,500 MW (from 114.9 GW to 119.4 GW). To avoid automatic off-peak scheduling, time-of-use 0 nighttime charging.

Figure C - 2: Thirteen NERC Regions, as of 2003, used in NEMS and other models. [Note: The ECAR region is no longer in operation.] Ultimately, the NERC Region formerly known as ECAR was chosen for th study for several reasons. First, ECAR inc ch were al Electric Power, FirstEnergy, and DTE Energy, with tran ISO. Many o ogy, and/or V2G technology. Thi West Virginia, whi Energy, American nterconn he benefi mix is domi region co ection and Midwes California). he effec Model's “ sufficient S of low amb highly cons , smart grid techno ich provided HG emissions origi , the colder winters in t ient temperatures on battery perform high technology” case, PHEVs are also forecasted to comprise just over 1 mill private vehicle fleet in 2030, which will supply a high enough additional load on the elec y examine the effects of PHEVs during specified charging periods. udes all or a por idered states. Sec f these organizati nsight on how G nating in a natura é site of ion of Ohio, Michigan ond, major utilities in smission ons have expressed in rd, ECA HG emissions originatin gas-dominated genera he Great Lakes area also presented the oppor ance. Finally, according to th he second regional case ndiana, Kentucky, and he region include Duke markets served by PJM erest in understanding R’s electricity generation g from PHEVs in this ion mix (southern unity to incorporate e ORNL’s MA°T he region’s id to ion of ric gr

Figure H - 1: PHEV Battery and Life Estimation Model.

Figure H - 3: Battery operating regime for PHEV applications - Sizing Example Li-lon Battery Energy Utilization vs. Time*

Figure H - 4: Base Case (PHEV-30) - SOC vs. C-Rate — Trip Work A

Figure H - 5: Base Case (PHEV-30) - SOC vs. C-Rate - Trip Work B

Figure H - 6: Base Case (PHEV-30) - SOC vs. C-Rate — Trip Errand A

Figure H - 7: Base Case (PHEV-30) - SOC vs. C-Rate - Trip Errand B

Figure H - 8: Base Case (PHEV-30) - SOC vs. C-Rate -— Trip Errand C

Figure H - 9: Base Case (PHEV-30) - SOC vs. C-Rate — Trip Weekend A

Figure H - 10: Base Case (PHEV-30) - SOC vs. C-Rate - Trip Weekend B

Figures H-11 through H-13 show the effects of varying AERs on SOC vs. C-Rate operating conditions, including a PHEV-10, PHEV-20, and PHEV-40. (Only the Work A plot is provided for each vehicle.) It is clear that for smaller systems, such as the one used in the PHEV-10, the battery will be operated more often at higher damaging conditions, higher c-rate, and charge sustaining at low SOC so additional batter capacity was required for these vehicles in order to reach the required 10-year lifetime.

Figure H - 12; PHEV 20- SOC vs. C-Rate - Trip Work A Figure H - 11: PHEV 10- SOC vs. C-Rate - Trip Work A

Figure H - 13: PHEV 40 - SOC vs. C-Rate - Trip Work A

Figure H - 14: Vehicle with 0% Weight Reduction - SOC vs. C-Rate - Trip Work A Figures H-14 and H-15 show the effects of varying vehicle weight on SOC vs. C-Rate operating conditions. In this study’s base case, vehicles in 2030 were assumed to have undergone a 30% reduction in glider weight relative to today’s vehicles. The figures investigate the same vehicles with no weight reduction and a 45% weight reduction. (Only the Work A plot is provided for each vehicle.) The lighter vehicle results in less strain on the battery, resulting in an increased projected lifetime.

Figure H - 15: Vehicle with 45% Weight Reduction - SOC vs. C-Rate - Trip Work A

Figure H - 16: Extended Work Cycle - SOC vs. C-Rate — Trip Work A Moaitications to Weekly Drive Uycies Mod was mod com ified driving habits also affect SOC vs. C-Rate operating conditions. In this case, the commute to worl extended by adding an HWFET cycle between the Work A’s existing UDDS and US06 cycles. The ified Work A plot is provided in the figure. As expected, the increased VMT from a longer daily mute decreases the projected life of the battery. A scenario with no weekend cycles was also conducted, which reduced lifetime VMT, resulting in increased the battery life expectancy. (Since this scen ario did not affect the work commute, no plot is provided.

Figure J - 1: Large office building (>30,000 square feet) total load in SCE. Summer Load Curve for All Large Office Buildings in SCE Territory

Figure J - 3: Fall loads for single large office building in SCE.

Figure J - 5: Change in load shape with PHEVs plugging in beginning at 7 a.m. Large Office Building Loads w/ and w/o Shifts from PHEVs A possible conclusion from this analysis is that the savings potential, while real, is not very significant for a arge facility. $2,000 per month in savings is only approximately 1% of the building’s electricity bill, and this amount does not cover the costs of installation or operation of the charging stations. Furthermore, it does not include any payment as incentive to the PHEV owners for their likely loss of battery life. The savings per each of the 35 PHEVs works out to between $30 and $60 per month. If the building owner chose to sp! he savings with the vehicle owners, each owner would likely receive around $250 per year each. This may or may not be a sufficient amount to entice some vehicle owners to allow use of their batteries throughout he day. Encouraging earlier arrivals so charging can begin sooner will help the overall savings, but the number of required vehicles will have to increase accordingly.

Figure K - 1: Thirteen NERC Regions used in NEMS and other models

Figure K - 2: AEO2009 Reference Scenario ECAR utility fuel costs The NEMS scenarios also project fuel prices for each region through 2030. Figure K-2 below shows the prices per mmBtu for each major fuel in the ECAR region. Natural gas rises to $10/mmBtu by 2030. Residual and distillate fuel oil prices, while high, were of miniscule importance for electricity generation. The prices shown include the effect of carbon cost adders. Without the carbon charge, the prices drop, especially for coal. Table K-3 shows the prices before and after carbon costs for each of the three ecanarios.

Figure K - 3: AEO2008 Reference Scenario California utility fuel costs For the California study, the AEO2008 reference case has what some would think of as relatively low future fuel prices. Figure K-3 below shows the prices per mmBtu for each major fuel in the California region. Natural gas stays between $6 and $8/mmBtu through 2030, although current prices (and the most recent forecast in the Short-Term Energy Outlook from EIA) are $11/mmBtu. Our study doubled the AEO2008 fuel prices for 2030 as the reference prices, but sensitivities were done using the AEO2008 prices and a quadrupling of those prices.

For ECAR, the 1940 power plant units from the NEMS data sets were aggregated by technology, fuel, and variable cost, into 195 plant groups (Figure K-4). While the unscrubbed coal plants are shown, they were retired and not used in the dispatching. The hydroelectric and pumped storage capacity are not shown, but totaled 1,900 MW for hydro and 3600 MW for pumped storage. As can be seen by the blue diamonds in the figure, the lowest variable cost sources were the nuclear, renewable, and coal with carbon capture and sequestration (CCS). Variable costs include fuel, operations and maintenance, and CO2 emissions costs. (NOx and SQ2 emission costs were zero because the national emissions had fallen below the ceiling set by EPA.) The must-run plants are a variety of distributed generation and cogeneration facilities that are not directly dispatched by the utilities, so their variable cost does not enter into the dispatch decisions. Coal plants and gas combined cycle (CC) plants had similar variable costs and so were intermixed during dispatching. Gas combustion turbines (CT) were most expensive due to the price of gas (including carbon costs) and their relatively low efficiencies.

Generation capacity factors are a function of each plant group’s relative variable cost, forced outage rate, anned outage rate, and the overall dispatch of the system's plants to the system’s demands. After eveloping the reference supply and demand amounts, and dispatching them in the model, the resulting eneration, capacity factors, and percent of total were compared to the AEOQ2009 reference scenario Table K-5). Coal generation is down slightly in the ORCED run, replaced by natural gas and renewables hydro and biomass). The renewable resources capacity factor is greater than 100% because it includes iomass that is co-fired with coal in coal capacity. Ssqe@ aT

Figure K - 6: ECAR hourly demands for 2030 based on escalating 2006 loads.

NEMS allows imports and exports between the various regions of the country based on available transmission capacity and internal calculations on relative cost of power. ORCED does not model the other regions in order to determine economic transfers in or out. They would be a function of the relative demands and supply in each region at any point in time. Instead ORCED increases the base demand amounts to represent the exports or lowers them to represent the imports. The remainder represents the loads that are met by the plants within the region. While California shows heavy reliance on imports in the NEMS analyses, the ECAR region roughly balances its imports and exports. For 2030, there is an import amount of roughly 2% of total generation. In earlier years, there are net exports of similar amounts.

Figure K - 9: Load Duration Curves for California loads before and after imports

For this phase, we updated the charging profiles for The amount of discharge for the various trips was re-evaluated in PSAT and th following the trips was calculated (Table K-8). The s begin and end the initial plugging in of their vehicle, not the time when the population of ve examp fourth at 0615, o percen e, if the start hicles is evenly distributed over art a he PHEV beyond the Cali nd end times represe he time period in 15-minu and end times are 0600-0700 th age of vehic es that plug in for that segment, eno ne fourth at 0630 and one fourth at 0645 as $ ne fourth of the vehic fornia study in several ways. € consequent recharging nt the times when owners hey unplug it. For our calculations e increments. So, for es plug in at 0600, one . These fractions are hown in Table K-8. further downscaled by the

Figure K - 12: ECAR demand with and without PHEVs on the peak week of the year

Figure K - 13: California demand with and without PHEVs on the peak week of the year The peak demand in California occurred about a week earlier than in ECAR, using the 2006 data as a iemplate. Figure K-13 shows the addition of the PHEVs on the hourly demand during this peak week. Note hat because in the California scenario we assumed 90% of the vehicles would charge at Level-1 (110V) lhe demand is spread more evenly over the nighttime load.

Figure K - 14: ECAR summer load duration curves with and without PHEV-added demand

Table K - 8: ECAR PHEV charging scenarios On Saturday all vehicles precondition in the morning. Ninety five percent of the vehicles recharge only on Sunday evening while 5% recharge on bot Saturday and Sunday evening.

First, a reference case was run to simulate the conditions from the HR2454cap scenario without PHEVs and the resulting wholesale electricity prices were found. These can be back-calculated to the corresponding demands to determine hourly wholesale electricity prices (Figure K-16). Prices average around 7.4 ¢/kWh, the marginal cost of many of the coal and natural gas combined cycle plants seen in Figure K-3. Daily peak prices are higher, especially in the summer when the annual peak is reached and prices climb up to 98 ¢/kWh. Because of the carbon charge of $65 per metric ton of COz, the variable costs from coal and natural gas combined cycle are around the same. As a result, prices do not stray far and depend on the price of gas and efficiency of the plant. Combustion turbines are called for only at peak times.

Figure K - 19: ECAR base capacity, base generation, and added generation with a $191/metric ton carbon cost mix of capacity and generation by 2030, even for the no-PHEV scenario. Figures K-19 and K-20 below show the base level capacity, generation, and added generation for each set of scenarios.

Figure K - 20: ECAR base capacity, base generation, and added generation with a $0/metric ton carbon cos

Table 1: A non-exhaustive list of announced plug-in electric vehicles slated for near-term production, as compiled by Sentech (last updated June 2010)

Table 3: Value propositions listed by lead investigator and required modeling tools or data

Table 4: Basis for vehicle cost calculations for mid-size sedan in 2030 (2010 $)

Table 5: Effect of PHEV peak shaving in July using SCE rates. Alternative charging scenarios were examined to potentially increase the amount of demand that can be shaved off of the peak loads. For example, if PHEV owners plugged in at 7 a.m. instead of 8 a.m., more vehicles could be fully charged, and the peak could be lowered by 80 kW compared to the previously mentioned 60 kW, as shown in Figure 16. The savings using the SCE rates doubles to $4,000 per month, though the number of PHEVs needed also doubles to around 70. A more detailed look at how potential benefits to commercial building owners were determined can be found in Appendix J.

Table 6: California 2030 generation and capacity factors from AEO 2008 and from ORCED simulations PHEV Value Proposition Study — Final Report / July 2010 The grid analysis for California covers the entire state, not just southern California, because the electric grid is operated as a whole. The CAISO creates a statewide market for electricity. For this analysis, the list of power plants owned by California utilities was determined from NEMS input file for EIA’s AEO 2008. The list of plants includes not only those plants within the borders but also plants owned by California utilities outside of the state, such as portions of the Palo Verde nuclear plant and the Intermountain Power project in Utah. California’s diversified mix from these plants shows a high percentage of generation from natural gas and renewables (Table 6).

Table 7: California study PHEV charging scenarios

Table 8: ECAR 2030 generation and capacity factors from AEO 2009 and from ORCED simulations

Table 9: Fuel and engine assumptions for both regional case studies Table 10: Base and marginal electricity generation mix assumptions for both case studies in 2030. In this study, PHEVs are assumed to almost always charge on the margin.

Emissions and total energy used are divided into categories: feedstock, fuel, and vehicle operation. Each represent the energy used in the processes ranging from resource recovery to refining and distribution, and the end use in the vehicle. (It should be noted that emissions and energy originating from upstream vehicle manufacturing were not included in this study.) Energy is also broken down into categories by source: coal, natural gas, and petroleum. In some cases, negative numbers in CO2 and GHG emissions are generated and demonstrate the fact that some of the feedstocks reduce CQz rather than contributing to it.

Table 11: Summary of parameters varied in this study's sensitivity analysis.

Table A - 1: A non-exhaustive list of announced plug-in electric vehicles slated for near-term production, as compiled by Sentech (last updated June 2010)

Table B - 1: 2030 Vehicle Parameter Assumptions

Table D - 2: Projected purchase prices for PHEVs in 2030 when Chapter 5's sensitivity parameters for AER are applied, which range from 10 miles to 40 miles.

Table D - 3: Projected purchase prices for PHEVs in 2030 when Chapter 5’s sensitivity parameters for battery cost are applied, which range from $100 / kWh to $400 / kWh.

Table E - 1: Characteristics of each individual EPA drive cycles incorporated into this study’s work, errand and weekend/vacation trips. Table E - 2: Description of the work, errand, and weekend/vacation trips exercised by each vehicle type in this study

PHEV Value Proposition Study — Final Report / July 2010

Table E - 6: Liquid fuel and electricity cost calculations based on consumption rates — 10-, 20- and 40-mile AER instead of base case’s 30 mile AER

Table E - 12: Liquid fuel and electricity cost calculations based on consumption rates — No glider weight reduction as opposed to base case’s 30% glider weight reduction

Table E - 14: Liquid fuel and electricity cost calculations based on consumption rates - 45% glider weight reduction as opposed to base case’s 30% glider weight reduction

Table E - 16: Liquid fuel and electricity cost calculations based on consumption rates - Modified Drive Cycles Compared to Base Case

ble E - 17: Liquid fuel and electricity (if applicable) consumption calculations for ICEs, HEVs, and PHEV-30s used in each regional case study - 2.5% grades (uphill and ywnhill) as opposed to base case’s 0% grade. In addition, three different glider weights were investigated. PHEV Value Proposition Study — Final Report / July 2010

Table G - 1: Emission and energy outputs for each regional case study, modeled in GREET Key emission and energy data for each regional case study, as modeled in GREET, is compiled in Table G-1. To recap, mid-size sedan models of ICEs, HEVs, and PHEV-30s were investigated. A 70/30 split of E10 and E85 was assumed in this study; hence an average fuel blend of E30 was used for modeling purposes. The vehicles were all assumed to have a glider weight of 30% less than today’s vehicles. Furthermore, a carbon tax of $65 per metric ton has been incorporated. Finally, the generation mixes used in each case study can be found in Section 4.4.2.

Table G - 2: Emission and energy outputs for all sensitivity cases, modeled in GREET

Table H - 1: EPA Drive Cycles used in the presented study In order to simulate statistically meaningful scenarios, typical days of driving were identified, reflecting the common driving habits, and average commute time of a typical user over a year. For a complete analysis, it is also important to consider different charging availability, i.e. how often it is possible to recharge the battery. Tables H-1 and H-2 summarize the driving/charging scenarios used in this study.

Table H - 2: Driving/charging sessions during typical week

Table H - 3: Summary of battery life estimation results

* Calculations are applicable to both case studies.

Table J - 1: SCE TOU-8 rates. SCE commercial rate structures are broken into both a demand and energy portion. The rates can vary based on the time of day and season in which they occur. Table J-1 shows the rates used for this analysis based on the Schedule TOU-8, Time of Use — General Service — Large published on the SCE website (SCE 2010). The summer season is June through September, while winter season is all other months. The analysis did not cover all of the intricacies of the rates, such as the combination of utility retained generating and Department of Water Resources energy rates.

Table J - 2: LADWP large general service rates.

Table J - 3: Effect of PHEV peak shaving in July using SCE rates.

Table K - 1: ECAR 2030 Generating Capacity for Three Supply Scenarios The EIA NEMS model calculates the power production and sales for the entire ECAR region as a whole through 2030. We determined the list of power plants operated within the region from NEMS input file for the Annual Energy Outlook 2009 (which are the same plants as for the HR2454 analyses). We then added the “unplanned” capacity that the model calculated as needed in each scenario from the EIA HR2454 analysis: Base, No CO2 Cost, and High CO2 Cost. Retirements were also accounted for. The resulting total capacity by technology in ORCED roughly matched the capacity defined in the each NEMS scenario (Table K-1).

Table K - 2: Projected California Generating Capacity 2030.

Table K - 3: ECAR 2030 Fuel Prices for three supply scenarios with and without CO2 cost.

Table K - 4: ECAR and California 2030 Base Fuel Prices with and without CO2 cost

Table K - 5: ECAR 2030 generation and capacity factors from AEO2009 and ORCED

Table K - 6: California 2030 generation and capacity factors from AEO2008 and ORCED Base Demand

Table K - 7: California study PHEV charging scenarios We set that 5% of the low-V vehicles and 10% of the high-V vehicles plug in between 8am and 9am after they reach work, with a refill required of 4.6 kWh. We further set that 10% of the vehicles would plug in for an hour during dinnertime. The low-V vehicles would only fill up 1.3 kWh while the high-V vehicles could recharge their full 4.6 kWh that was used driving home. At night all vehicles plug in for charging: the 90% of them that did not charge at dinner requiring 5.3 kWh, while the 10% that did charge at dinner needing less (low-V taking 5.1 kWh, high-V just 1.8 kWh.) Over the weekend the plugging in would only be at nighttime and, according to the drive cycles used, would need 7.9 kWh to fully recharge the battery.

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Multivariant design and multiple criteria analysis of building refurbishments

by NuSha NaZir

2005, Energy and Buildings

In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great... more

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A Method of Drag Reduction of a Vehicle by Computational Investigation and Geometric Modification

by Shivaji Shinde

The need for fuel efficiency is a rapidly increasing trend in automotive industries in the recent years. Therefore, extensive research is undergoing for development of aerodynamically optimised vehicle designs. The drag coefficient is an... more

Boundary conditions Sambit Majumder and Somnath Saha Table 2: Properties of air

Table 4: Turbulent intensity and turbulent viscosity ratio Energy equation was not incorporated in the simulation as there was no thermal involvement. Coupled scheme, pressure-velocity coupling was used as the iterative algorithm because this algorithm has more robust and efficient single phase implementation for steady state flows. Hybrid initialization method was used for initializing the simulation. Both first order upwind and 2" order upwind discretization scheme were used for the momentum, turbulent kinetic energy and turbulent dissipation. Sambit Majumder and Somnath Saha

simulation as shown in Fig. 3. To get efficient solution, computational domain was set at 2L (L=Length of the vehicle) either side of the models and 3L upstream and downstream of the models. This size of the domain was efficient enough to capture the changes in the flow field [12]. The models were meshed in commercial package ANSYS 14. In both the cases, the domain consisted of tetrahedral mesh and prismatic mesh as shown in Fig. 4. Prismatic mesh was incorporated in space around the vehicle, wheels and road. Prismatic mesh can capture surface forces of the vehicle accurately and improve accuracy of the calculation. Boundary Layers were applied by inserting program controlled inflation consisting of First Aspect Ratio as 5, 5 prism layers and 1.2 growth rate as shown in Fig. 5. A fine mesh is required to resolve the vortices behind the vehicle body. Therefore, the mesh was refined at places where the parameters were to be accurately obtained. Hence, local refinement of the domain around vehicle’s body was done. Another regular domain was made around the vehicle to coarsen the space around the vehicle with still smaller meshes to reduce computational effort and obtain more detailed flow information. The domain was extended 2 L upstream and L downstream, whereas 2 L above the vehicle’s roof. Tetrahedral grid was used to mesh the computational domain. The grid topology is shown in Fig. 6. Number of nodes and elements for both cases are shown in Table 1.

A Method of Drag Reduction of a Vehicle by Computational Investigation

Fig. 7: Velocity contours (a) Base Vehicle (b) Vehicle with rear plate It can be concluded from Fig. 8 that there is a decrease in negative pressure distribution at the rear end of vehicle. Velocity contour plots depict that there occurred a redistribution of vortices at the rear end after introducing rear plate. The same can be clearly seen in the streamline contours shown in Fig. 10. It has been observed that, in vehicle with rear plate two additional small vortices are generated between the plate and rear of the vehicle. In Table 5 various planes were shown at different distances from the rear of both the vehicles to depict the changes in velocity contour. In Table 6, a comparative study of velocity distribution of both the vehicles at these locations was shown. Here, y* = y/L and U* = u/ux where, y* = non- dimensional length, U* = non-dimensional velocity, u = local velocity, u. = free stream velocity, L = length of the vehicle and z = positions in z-direction.

Fig. 10: Streamlines (a) Base Vehicle (b) Vehicle with rear plate

Fig. 8: Pressure contours (a) Base Vehicle (b) Vehicle with rear plate A Method of Drag Reduction of a Vehicle by Computational Investigation

Table 5: Velocity contours at different positions behind the vehicles.

\ Method of Drag Reduction of a Vehicle by Computational Investigation

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Optimal Power Management for a Hydraulic Hybrid Delivery Truck

by Dennis Assanis

2004, Vehicle System Dynamics

Hydraulic hybrid propulsion and energy storage components demonstrate characteristics that are very different from their electric counterparts, thus requiring unique control strategies. This paper presents a methodology for developing a... more

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Predictive energy management of a power-split hybrid electric vehicle

by Ming Kuang

2009, 2009 American Control Conference

In this paper, a Model Predictive Control (MPC) strategy is developed for the first time to solve the optimal energy management problem of power-split hybrid electric vehicles. A power-split hybrid combines the advantages of series and... more

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Effect of Fuel Magnetism by Varying Intensity on Performance of Single Cylinder Four Stroke Diesel Engine

by IJSRD Journal

The aim of this study is to investigate the effect of the fuel magnetisation on the performance of diesel engine. It has been observed that on magnetisation viscosity of hydrocarbon fuel decreases due to declustering of the Hydrocarbon... more

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ADVISOR: a systems analysis tool for advanced vehicle modeling

by Keith Wipke

2002, Journal of Power Sources

This paper provides an overview of Advanced Vehicle Simulator (ADVISOR)-the US Department of Energy's (DOE's) ADVISOR written in the MATLAB/Simulink environment and developed by the National Renewable Energy Laboratory. ADVISOR provides... more

Fig. 2. ADVISOR simulation setup window.

Fig. 3. ADVISOR results window. Finally, the actual vehicle model is composed of a collec- tion of component models. In Fig. 5, it can be seen that the individual component models are stored in a library. The component models can be inserted into a vehicle model and then connected to define the flow of torque/speed and power

Fig. 4. ADVISOR interaction between data files, GUI, and model (block diagram).

Fig. 5. ADVISOR model definition employs linked library architecture. power request from one component to the next upstream component. The request is initially based on the vehicle speed requirements and is modified as it passes through each component to account for losses associated with each com- ponent. Arrows entering the bottom input port of each block represent what the upstream component is able to achieve. Typically, this will be the same as that requested unless a The arrows entering the top input of a component block in the vehicle model in Fig. 5 depict a torque and speed or a

Fig. 6. NREL to Vail, Colorado drive cycle.

4.3. Limitations Fig. 7. Acceleration test results for a typical conventional vehicle.

Fig. 8. Typical engine fuel consumption data. “ 4 “e Since the fuel consumption and the engine-out emissions will be adjusted based on engine temperatures, it is important that accurate tracking of engine temperatures is maintained.

Fig. 11. Fuel cell stack polarization curve input data. Finally, the third fuel cell system model is achieved via a simulation link between ADVISOR and the General Com- putational Toolkit (GCtool). GCtool is a software package developed and maintained by Argonne National Laboratory for modeling complex thermodynamic and chemically reacting systems [6]. GCtool can represent systems from simple stacks all the way to complete systems including a reformer. When using GCtool with ADVISOR, the GCtoo model is first called independently in a design mode to determine the system characteristics (i.e. fuel cell dimen- sions, flowrates, and pressures) necessary for the desired peak operating conditions. The configuration details are saved. During the simulation, these configuration parameters are held constant while the operating power level is varied

Fig. 10. Typical input data for net power vs. efficiency fuel cell systen model.

Fig. 12. Parasitic load input data for polarization curve model.

Fig. 14. Resistance-capacitance (RC) battery model electrical schematic. Fig. 13. Internal resistance battery model electrical schematic.

Fig. 15. PNGV battery model electrical schematic. The fifth model type is a fundamental battery model (Fig. 17). As with GCtool for fuel cells, Simulink has been used in this case to link to a compiled and executable battery model. Prof. John Harb of Brigham Young University

Fig. 18. NREL’s Battery Thermal Management Laboratory. The majority of the input data for the battery models included with ADVISOR has been collected in the Battery Thermal Management Laboratory. This facility (Fig. 18) not only provides model input data but also the ability to validate the battery model results.

Fig. 16. Neural network battery model block diagram.

Fig. 19. ADVISOR linkage to optimization tools. — Vehicle system optimization is also an area in which ADVISOR excels. Various optimization algorithms have been wrapped around ADVISOR (Fig. 19) to automate the iterative process of design improvement. The solution

Fig. 20. Battery capacity required to support electrical accessory loads.

Fig. 21. Energy usage analysis of conventional and advanced technology vehicle.

Technology improvements for energy balance analysis Table 1 An energy usage analysis can help focus technology development on areas that can have the greatest impact on energy conservation efforts. ADVISOR provides the capability to do this type of analysis quickly for advanced technology vehicles operating over entire drive cycles.

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Virginia Tech Comprehensive Power-Based Fuel Consumption Model: Model development and testing

by Eric Van den Bulck

2011, Transportation Research Part D-transport and Environment

Existing fuel consumption and emission models suffer from two major drawbacks, namely: (a) they produce a bang-bang control through the use of a linear power model (i.e. the optimum fuel consumption requires a full throttle acceleration);... more

Figure 1: Typical power vs. fuel consumption functional form Consequently, two power-based second-order polynomial models are proposed in this paper. These models are entitled the Virginia Tech Comprehensive Power-based Fuel Consumption Model-1 and -2 (VT-CPFM-1 and VT-CPFM-2). The use of a second-order mode with a positive second-order parameter is required in order to ensure that a bang-bang control does not result from the application of the model, as was described earlier in the paper. Additior of higher than second-order parameters would add to the complexity of the model and thus not allow for model calibration using the EPA city and highway cycles. Consequently, a second- order model provides a good compromise between model accuracy and applicability. The two VI-CPEM models are formulated as

As was mentioned earlier, the first model does not require engine data to estimate vehicle fuel consumption rates. The idling fuel consumption rate for the VT-CPFM-1 model is estimated using Equation (7) and bounded using Equation (8) to ensure that the functional form is convex, as will be discussed later in the paper. The idling fuel consumption rate in Equation (7) is an average operating point method that was proposed by Guzzella and Sciarretta. It should be noted that in reality the idling fuel consumption rate constantly fluctuates, however the proposed mode assumes, for simplicity purposes, that the rate remains constant. Here Py. is the idling fuel mean pressure (400,000 Pa); ia is the idling engine speed (rpm); d is the engine displacement (liters); O is the fuel lower heating value (43,000,000 J/kg for gasoline fuel); N is the number of engine cylinders; Fin, and Fy, are the total fuel consumed for the EPA city and highway drive cycles (liters), respectively (computed using Equations (9) and (10), respectively); Tein, and Thwy are the durations of the city and highway cycles (1875s and 766s, respectively); Pein, and P’ cir, are computed as the sum of power and power squared exerted each second over the entire cycle (computed using Equations (11) and (12), respectively). Similarly, Pj, and Pinwy are estimated in the same manner for the highway cycle (computed using Equations (11) and (12), respectively). The ¢ term ensures that the second-order parameter (Q2) is greater than zero. Experimentation with the model revealed that a minimum value of 1E- 06 ensures that the optimum fuel economy cruising speed is in the 60 to 80 km/h range which is typical of light-duty vehicles.

Figure 3: Instantaneous Model Validation, Honda Accord.

Figure 4: Instantaneous M odel Validation, City Cycle.

Figure 6: Simulated Fuel Economy for Different Driving Cycles.

Table 1: List of Parameters Required for Model C alibration

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Influence of gasoline engine lubricant on tribological performance, fuel economy and emissions

by Martin Priest

2007, … economy and emissions, London, 11-12 …

The requirement for increased performance, improved fuel economy and reduced emissions is constantly sustaining the demand for research into combustion, fuels and lubricants. Due to the nature of the operation of an engine and the current... more

The lubricant influences the tribological performance, fuel economy and the emissions of the internal combustion engine. The interaction between the lubricant, the piston ring pack and its environment plays the most significant role in the control of oil consumption, affecting emissions, fuel economy and fuel economy retention through friction, and overall engine performance, such as wear. It is an artefact of the design of the ring pack and the need for there to be some lubrication between rings and liners that a small amount of oil passes the top ring and enters the combustion chamber. This results in oil consumption as it is lost to the exhaust where it adds to the emissions. This consumed oil also reduces the effectiveness of the gasoline after-treatment catalysts by forming a film of phosphorus over the catalytic surface. For this reason oil manufacturers are being legislated to reduce the quantity of sulphur and phosphorus contained in engine oil (from 0.12% in 1994 through 0.10% in 2001 for GF-3 to 0.08% in 2004 for GF-4). Some of the thinnest oil films in an engine are found between the top ring and the liner, at times operating in boundary lubrication conditions, as shown in Figure 1, hence permitting contact between these components, and it is partly for this reason that the piston assembly accounts for 40-60% of total engine friction.

Figure 3 - TRZ oil carbonyl levels and sump carbony] levels (x100), after [3] Figure 2 - TRZ piston (a) and schematic of engine (b) with sample pipe, after [10]

Additional work into the chemical mechanisms of the degradation of the oil was undertaken at the Chemistry Department, University of York. Readers interested are directed to papers from that institution [3, 7-9]. Some results for carbonyl concentration are presented in Figures 3 and 6, and described in section 2.3.

Figure 5 shows the friction results obtained from this extracted oil when tested in a Plint TE-77 reciprocating tribometer. The test parameters were set to simulate the ring and liner contact conditions in the most severe conditions just after combustion and the material used was that of the engine liner for both the pin and the plate. It is clear from the figure that there are distinct differences between all the samples, but most notably between the sump oil and fresh oil samples and the four top ring zone samples which is the oil that lubricates the ring/liner interface.

Figure 42 - Volatile content of oil samples measured by GC, after [4] *TRZ =top ring zone

By taking both TRZ samples and sump samples, and measuring the carbony] concentration of both sets of samples it is possible to calculate the flow rates of degraded oil from the ring pack to the sump. This is because if the sump is maintained at 70°C, a temperature at which negligible degradation occurs, the degradation of the sump oil is caused by the degraded oil returning from the ring pack. This results in a steady increase in degradation in the sump over time. Figure 6 shows the carbony] species in the sump increasing (b) compared to the fairly constant level of degraded sample taken from the ring pack (a) with respect to time. The results presented are for the same speed, 1500 r/min, and three different loads as indicated on the graph. The 50% load experiment was undertaken twice to investigate repeatability. The carbonyl content does not start at zero for the sump oil at the start of the test. This is because, despite flushing the engine, residual carbonyl from previous tests was present in the sump oil.

Initial research [12], Figure 8, suggests that a higher viscosity lubricant will flow through the ring pack more slowly than a lower viscosity lubricant. Therefore as lubricants degrade and hence become more viscous they may remain in the ring pack, exposed to the degradation process, for longer. Although the speed at which the oil in the ring pack degrades will remain the same it will become highly degraded due to being present for longer and so it is likely to result in higher levels of sludge, varnish and then deposit formation before the lubricant is due to flow back to the sump. This will have additional detrimental effects on fuel economy over and above the increased viscosity of the lubricant as well as affecting the action of the rings and hence ultimately affecting emissions as they will no longer seal against the bore correctly.

Table 2 - Residence times in the Piston Assembly with respect to engine speed and load, after [12]

Figure 9 - Comparison of piston assembly friction power loss for SAE OW 20 without FM (friction modifier) and SAE 5W30 with FM lubricants, after [14] The vast majority of the frictional losses in an engine are attributable to the three main tribological sub- systems of an engine; the piston assembly, engine crankshaft bearings and the valve train. Experimental methods have been developed for the Hydra engine at Leeds that allow the losses in all three tribological components to be measured while the engine is fired. This involves the use of specially designed torque transducers on the drive pulleys of the valve train, the indicated mean effective pressure technique to obtain the piston assembly friction and the calculation of crankshaft bearing losses by subtraction. This method has confirmed that between 40 and 60% of the total engine friction is due to the piston assembly and has shown the effect of speed and lubricant viscosity on power-loss in the piston assembly, Figure 9. This work has been reported by Mufti et al [13, 14].

Figure 10 - TE77 piston assembly friction coefficients for non additivised oil and three different additivised oils.

elements. It is possible for these volatiles to enter the gas state and be drawn into the exhaust gas, having a detrimental effect upon emissions. With consideration to future problems posed to lubricant formulation, initial work has suggested that bio-fuels are likely to have increased absorption levels into the TRZ oil and, ultimately, the sump. This will change the sump oil formulation from the optimum designed and may effect the frictional characteristics of the oil throughout the engine resulting in reduced fuel economy.

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Assessment of cow dung as a supplementary fuel in a downdraft biomass gasifier

by niladri datta

2010, Renewable Energy

A model of downdraft gasifier has been described considering thermodynamic equilibrium of species in the pyro-oxidation zone and kinetically controlled reduction reactions in the reduction zone. It is found that the sole use of cow dung... more

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Plug-In Hybrid Electric Vehicle Value Proposition Study: Phase 1, Task 3: Technical Requirements and Procedure for Evaluation of One Scenario

by karen Sikes

2008

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Clean fuels from municipal solid waste for fuel cell buses in metropolitan areas

by Ernst Worrell

1996, Resources Conservation and Recycling

Increasing volumes of municipal solid waste (MSW) pose disposal problems for many cities. Costs are rising as landfilling becomes more difficult. The production of clean transportation fuels (methanol or hydrogen) from MSW is one... more

Fig. 1. Process steps in the production of methanol! and hydrogen from municipal solid waste. With

some gasifier designs, not all process steps shown would be required. For example, the Thermoselect
gasifier requires no preprocessing or reforming steps.
Gasification refers to the conversion of a solid feedstock (by heating in an oxygen
starved atmosphere) into a gas suitable for combustion or for thermochemical
processing. The key components in gasifier product gases include H,, carbon
monoxide (CO), carbon dioxide (CO), methane (CH,), and higher hydrocarbons.
The smaller gas volume compared with combustion reduces the required capacity
(and hence cost) of any gas cleanup equipment needed downstream of the reactor.
Gasification is widely used with coal. Some woody-biomass gasifiers are also
operating commercially in applications where the product gas is directly burned in
a boiler or kiln. A number of pilot-scale MSW gasifier designs have been tested
over the past couple of decades [7]. Currently, there are two commercially operating
MSW gasification plants in the world, both in Italy. (The larger of these is in Greve,
Italy. It is an air-blown RDF gasifier built by TPS (Sweden), and the gas is fed to
a gas engine and a clinker kiln (for cement production). It is not discussed further
in this report. As an air-blown gasifier, it produces a synthesis gas diluted with
nitrogen and is therefore unsuitable for fuels production. The other gasifier in Italy
is of the Thermoselect design discussed in this report.)

Fig. 2. Estimated levelized costs of methanol production from MSW as a function of the tipping fee for
production systems using three different gasifiers. Details of the cost estimates are given in Table 8. For
comparison, the tipping fee at New York City’s Fresh Kills landfill is shown. Also shown are the
estimated costs of producing methanol from natural gas, assuming the average industrial-sector price of
natural gas in 1994, and as forecast for 2010 by the US Dept. of Energy, as well as the range in methanol
wholesale prices in the USA from 1984 to 1992.
£.D. Larson et al.| Resources, Conservation and Recycling 17 (1996) 273~298

E.D. Larson et al. | Resources, Conservation and Recylcing 17 (1996) 273-298
Fig. 3. Comparison of tipping fees required for fuels production from MSW and for electricity
generation from MSW in a mass burn incinerator to compete with alternative sources of fuels and
electricity. The cost ranges for H, and CH,OH from MSW correspond to costs with the BCL gasifier
(lower edge of range) and with the Thermoselect gasifier (upper edge of range). For details of fuel
production cost calculations see Table 8. For detailed electricity cost assumptions, see Fig. 6 in [221.

Fig. 4. Estimated range in fuel costs per km (excluding taxes) for a 12 m PEM fuel cell bus operating
on H, produced from MSW, with comparisons to fuel costs per km for diesel engine buses for two diesel
fuel prices. The cost range corresponds to costs with the BCL gasifier (lower edge of range) and with the
Thermoselect gasifier (upper edge of range).
gasification systems examined here, a minimum tipping fee ranging from $66 to
$104 per t would be needed to produce CH,OH at 7.5$/GJ (Fig. 2). For compari-
son, the average tipping fee at the Fresh Kills, New York City landfill today is $74/t
(P. Bekoweis, New York Department of Sanitation, pers. commun.). Tipping fees
in other areas in the Northeastern USA (and Western Europe) are even higher, e.g.
77$/t in Philadelphia and $121/t in Newark, NJ [27]. Thus, the low estimates for
costs of CH,;OH production from MSW (BCL gasifier) would be competitive at any
of these locations with making CH,OH from natural gas at the 1994 average US
price of gas. The relative economics of CH;OH from MSW versus natural gas can
be expected to improve further in the future, because natural gas prices are forecast
to rise [28]. If tipping fees rise in the future, which seems likely, the economics of
fuels from MSW would improve still further.

E.D. Larson et al.| Resources, Conservation and Recylcing 17 (1996) 273-298
Fig. 5. Estimated total lifecycle costs per km (excluding taxes) of purchasing and operating a 12 m,
wheelchair equipped H,-PEM fuel cell bus in New York City with H, made from MSW and for discount
rates of 10% and 5%. The cost ranges correspond to costs with the BCL gasifier (lower edge of range)
and with the Thermoselect gasifier (upper edge of range). At each discount rate, the upper shaded region
assumes that levelized annual O&M costs are the same for a fuel cell bus as estimated for a diesel bus
today. The lower range assumes the O&M cost is two-thirds of the diesel O&M cost. See Table 9 for
details.

Based on Chen [7]. HHV, higher heating value.
Performance estimates for three MSW gasifiers based on pilot-scale testing

Overall energy balances for methanol and hydrogen production from MSW
Based on Chen [7].

n.a., not applicable.

“Electricity is generated in a steam turbine, with the steam raised using waste heat.

>For facilities using the BCL gasifier, there would be a net surplus of electricity that could be sold.
‘The energy ratio is defined as the higher heating value (HHV) energy content of the fuel produced
divided by the HHV energy content of the raw MSW feed to the plant.

‘The thermal efficiency (TE) is defined as the HHV energy content of the fuel produced divided by the
HHV of all energy inputs, including the raw MSW feed and the primary energy that would be needed
to generate the purchased electricity. For the latter, for consistency, it is assumed that the electricity
would be generated in a gasifier/gas turbine combined cycle using MSW as the gasifier feedstock.

Estimated quantity of annual wastes potentially available in New York City for energy use
“The ‘as-received’ moisture content of the waste streams included here vary considerably with geography
and season. An across-the-board average of 25% is probably not unreasonable {7]. Similarly, the heat
contents of different materials differs considerably. An across-the-board average of 10.0 GJ/t (as-re-
ceived) is a reasonable estimate.

>As estimated by LaValva (New York City Dept. of Sanitation, 1994, pers. commun.). Included in the
figures for each borough are food wastes, yard wastes, unrecyclable and soiled paper products, and other
miscellaneous compostable materials such as diapers.

*This is an estimate made for the Department of Sanitation [8}. It includes 1.23 Mt of ‘dry urban wood’
(construction scrap, demolition debris, and crates and pallets), 0.05 Mt/year of ‘green wood’ (from tree
trimmings and land clearings), and 0.04 Mt of ‘special use wood’ (treated wood such as rail ties,
telephone poles, and pilings).

“This is our estimate assuming that (i) an average of 7% of the waste stream is plastics (the estimated
US average [7]), half of which is recyclable, and (ii) a total waste stream of 4.8 Mt/year (based on
estimated total waste stream of 15 400 t/dav [1] and 312 collectinn dave ner vear)

Measured contaminants in flue gas from combustion of cleaned Thermoselect gasifier product gas, with
comparisons to US and European MSW incinerator flue gas emission guidelines
“Units are mg per normal m* unless ng per normal m*. Normal conditions are 0°C and | atmosphere
pressure.

These are the average of measurements (adjusted to 7% oxygen) taken on three separate days at the
Fondotoce (Italy) gasifier [13]. The measurement point is the flue gas stream leaving the combustor in
which heat for the pyrolysis chamber is generated by burning cleaned product gas. Product gas cleanup
before combustion includes shock cooling through direct contact with water to below 90°C immediately
following the gasifier, followed by multi-stage alkaline washing, and a final filtering through activated
charcoal.

“The US guidelines are proposed new source performance standards for Municipal Waste Combustors

Results of toxicity tests on ash from RDF gasification with the MTCI gasifier [18]

The methanol bus estimates are from-Georgetown [23]. The hydrogen fuel cell bus emissions estimates
are from Steinbugler (CEES, Princeton University, 1995, pers. commun.).
Measured tailpipe emissions (in g per kW h) from hydrogen and methanol powered fuel cell buses
compared with the 1998 Federal emission standards for heavy-duty internal combustion engine vehicles

“The net carbon emissions associated with hydrogen or methanol production from MSW are detailed in
the lower section of this table. It is estimated that a hydrogen/fuel cell bus of comparable duty to the
diesel bus described in footnote b below would require 16.7 MJ/km of hydrogen fuel (for a PEM fuel
cell bus on a typical intracity bus cycle (Howard, Ballard, 1995, pers. commun.). A comparable
methanol/fuel cell bus) is estimated to require 19.5 M3J/km (for a phosphoric-acid fuel cell bus [11]). The
tates of methanol and hydrogen production per t MSW are detailed in Table 2.

>The assumed fuel requirement for a diesel bus is 2.9 MJ/km (1.3 km/l), the average for New York
(Pellegrin, New York City Transit Authority, 1995, pers. commun.), and the carbon content of diesel
fuel is assumed to be 18.6 kg/GJ. Carbon emissions associated with crude oil recovery from the earth,
transport to refinery, and refining to diesel are not included in this comparison. Assuming these
emissions would be comparable to those associated with gasoline production [24], adding them here
would increase the total emissions per bus-km by about 20%.

“The BCL and MTCI gasifiers use RDF, while the Thermoselect gasifier uses raw MSW. For the BCL
and MTCI gasifiers, the yield of RDF is 0.83 dry t/dry t of MSW arriving at the plant gate. The 17%
of the original dry MSW mass that is lost in this conversion is assumed to contain negligible carbon.
“The primary carbon releases are CO, emissions from the combustion sections of each of the three
gasifiers and (for methanol production) from the Selexol acid gas removal system and (for hydrogen
production) from the steam reformer furnace.

“Assuming that two-thirds of the carbon in MSW js accaunted far hy ranawakly canecetad ecneschod.
Lifecycle carbon emissions from methanol or hydrogen production from MSW and use in fuel cell buse:
compared with emissions from diesel-fueled internal combustion engine buses

“The diesel engine bus fuel economy is the current New York City average (Pellegrin, New York Transit Authority, 1
pers. commun.). (The average for the Borough of Manhattan is 1.1 km/I, due to the low average bus speed in Manha
— 5.8 km/h). The fuel economy for the fuel cell bus is 2.3 km/lI-diesel equivalent), the average of the range estim
by Howard (Ballard, 1995, pers. commun.) for a Ballard hydrogen/PEM fuel cell bus on an intracity drive cycle
The assumed bus lifetimes of 12 years for the diesel and 18 years for the fuel cell correspond to the replacement sche
authorized by the Federal Transit Authority (FTA) for diesel and electric-trolley buses, respectively. The FTA prov
funds to cities for the purchase of public buses. See footnotes c and d below. :

“The average annual travel distance per bus in New York City is 40 000 km (Pellegrin, 1995, pers. commun.). In
where average bus speeds are higher, the average annual bus travel is probably closer to 67 000 km. See footnote d be
“The Federal Transit Authority, which provides funds to urban transit authorities for purchase of buses, authorizes
retirement after 12 years’ operation, over which time it is assumed that an average of 800 000 km would be trave
The FTA standard for electric trolley buses is 18 years/1 200 000 km. These standards imply an average annual mile
of 66 700 km. The New York City average is 40 000 km, because bus speeds there are lower than the average for
urban areas. For 12 and 18 year lifetimes, total bus-km in New York City are 480 000 and 720 000, respectively. Assun
800 000 and 1 200 000 km instead would not significantly change the tipping fees at which the lifecycle cost per mile
the fuel cell bus would compete with that for the diesel engine bus.

‘The capital cost for the diesel bus, $216 900 (or $237 000 in 1995$), is based on a recently contracted price for di
buses by the New York City Transit Authority (Pellegrin, New York Transit Authority, 1995, pers. commun.). The car
cost for the fuel cell bus, $297 400 (or $325 000 in 1995$), is an estimate for mass production of a commercially mat
hydrogen/PEM fuel cell bus from Ballard (Howard, Ballard, 1995, pers. commun). The market entry price for the

cell bus (production of about 100 units) is estimated to be $500 000 to $600 000 (Howard, Ballard, 1995, pers. commu
"This diesel fuel price, $6.5/GJ (25 cents/l), which excludes 11 cents/l state plus federal taxes, is projected by the Departn
of Energy for the New England census region in 2010 (EIA, 1995). The hydrogen price includes the production cost u:
the BCL gasifier (Table 8, which assumes a discount rate of 9.91%) plus $4.9/GJ [24] for distribution and filling stat
costs.

*The H, price includes production cost (Table 8) plus 4.9$/GJ [24] for distribution and filling station costs. The ‘1
fuel cost is with the BCL gasifier. The ‘high’ fuel cost is with the Thermoselect gasifier.

"For the diesel bus, these are O&M costs based on the standard maintenance schedule, procedures, and replacem
parts of the New York City Transit Authority (Smith, New York Transit Authority. 1995. pers. commun ) ascurr
Cost comparison for New York City of 12-m, wheelchair-equipped diesel engine and fuel cell buses

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Plug-In Hybrid Electric Vehicle Value Proposition Study: Phase 1, Task 3: Technical Requirements and Procedure for Evaluation of One Scenario

by Vincenzo Marano

2008

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Automobile Externalities and Policies

by Ian W Parry

2007, Journal of Economic Literature

Externalities and Policies I a n W . H . P a r r y , M a r g a r e t W a l l s , a n d

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Benchmark of thin film coatings for lubricated slip-rolling contacts

by G. Theiler and

2010, Wear

The light-weight approach and fuel economy targets in today's automotive engineering require tribosystems, which can withstand higher contact pressures associated with low coefficients of friction. The application of high-performance... more

Fig. 1. Sample configuration in a twin-disc tribometer of the Amsler-type and arrangement of the sensor for the detection of the acoustic emission [21 Thrust bearing discs made of 100Cr6H coated with different Cr/W-C:H coatings were tested and validated under lubricated rolling motion at Pmax of 1.6 GPa (corresponding to FZG=10-11) [14,15]. With a SAE 10W-40 oil (HTHS=3.93 mPas) as lubricant, they reached between 0.5 and 2 billion cycles. Recently a range of coatings and surface treatments were eval- uated under slip-rolling contact with a slip level of 0.5%. Tests were run lubricated in grease and dry at a rotational speed of 400 rpm and at Po max = 3.65 GPa (1500N) [20]. In dry conditions, the vibro-polishing surface treatment showed excellent wear resis- tance during 50,000 cycles. In lubricated conditions, all treatments and coatings tested during 10,000 to 50,000 cycles gave low fric- tion and wear, particularly the tungsten carbide coating, a solid lubricant coating with graphitic microcrystalline structure and a third coating system based on a DLC (ta-C:H) on top of plasma nitriding.

Fig. 2. Evaluations of the minimal oil film thickness for three oils at RT and 120°C [28,29,30] following the equation of Hamrock and Dowson.

Fig. 4. Slip-rolling resistance of the DLC-coatings of supplier ‘G’ at RT. The test conditions (Po mean = 1.5 GPa, 10% slip, paraffinic oil, and Room Temperature (RT)) have since then been selected as stan- dard starting conditions for the screening of all further coatings to come. Achieving 10’ revolutions/cycles despite the augmen- tation of the admissible contact pressure Po mean from 1.5GPa to 2.0 GPa, underlines the functional advancement accomplished for such hard coatings. Only coatings with such a performance under the above-mentioned standard conditions were put forward as can- didates for further, more demanding test conditions such as higher pressure, higher temperature (120 °C/250 °F) or different lubricants (i.e., formulated oils), etc. The ta-C coating G was one of the first in this benchmark, which fulfilled the more demanding tests. This layer, however, has a high roughness due to the deposition pro- cess, leading to an intense and fast polishing of the counter body. An additional polishing step (labelled c.p. in Fig. 4) of the coat- ing was introduced to reduce the roughness, unfortunately, this An overview of the slip-rolling resistance of the DLC coatings from the manufacturer ‘I’ is presented Fig. 5. The numbers 01-11 represent the coatings I-01 to I-11, which are slightly different from one to the others as mentioned in Table 4. The evaluation of these coatings started at RT in different lubricants [32]. The positive test behaviour of the coatings under standard testing conditions allowed an increase of the initial average Hertzian contact pressure

Fig. 3. Slip-rolling resistance of the DLC-coatings of supplier ‘D’ at RT.

Fig. 5. Benchmark of the slip-rolling resistance of various DLC-coatings of supplier ‘I’.

Fig. 6. Influence of the substrate on the slip-rolling resistance of two DLC-coatings of supplier ‘J’ where Cis the load capacity and p an exponent between 3.8 and 4.2 (a value which is typically 3 for uncoated steel ball bearings or 10/3 Furthermore, the first results carried out at 120°C in poly(propylene)- and poly(butylen) glycol point out the outstand-

Fig. 7. Slip-rolling resistance of the DLC-coatings of supplier ‘M’ at RT.

Fig. 8. Slip-rolling resistance of the DLC-coatings of supplier ‘P’. The influence of the substrate on the rolling resistance was also investigated with the M-coating (Fig. 7). Therefore, both the SV30 as well as the 100Cr6H substrates were coated with the M-Material in the same process and batch. The coatings on the different sub- strates were then tested until a damaged surface area reached the same size (i.e., the 1mm? failure criterion), but not longer then 10 million cycles. Although this coating on 100Cr6H is slip-rolling resistant at Pp mean = 1.75 GPa in unadditivated paraffinic oil at RT, at Po mean = 1.94 GPa this coating lasts about 5 x longer on the Cronidur 30 substrate than on the 100Cr6H substrate. However, in the case of the factory fill oil at 120°C and Pomean =1.5 GPa, the tendency seems to be that the slip-rolling resistance of the M-coating on the 100Cr6 substrate is larger than on the Cronidur 30 substrate. Considering these results, this coating is at least as loadable as the ones of supplier I (coating I-04) or of supplier J (coating B). This figure suggests a more pronounced dependency of the slip- rolling resistance on the conditions of the deposition process as well as on the coating-substrate interface, thus underlining the complexity of the optimisation of the slip-rolling resistance. The synergy of the coating B and the substrate SV30 gives an excellent rolling resistance of the coating B in paraffinic oil at RT as well as in factory fill oil at 120°C under a high pressure of Pop mean = 1.94 GPa and exhibited almost no damage, fatigue or coating wear, even after 10 million cycles. Even the use of a fully formulated engine oil at

Fig. 10. Optical microscopy images of uncoated discs and their counterbodies after test at Po mean = 1.5 GPa, n= 10’ cycles (the bars on the images represent 500 jum.). One further DLC coating system included in this benchmark comes from the manufacturer P. Fig. 8 shows the good performance of the coatings on SV30 substrate in PPG and PAG at 120°C and Po mean = 1.5 GPa. However and contrarily to most of the results seen up to now, this coating seems to be in factory fill oil more slip-rolling resistant on a 100Cr6H substrate than on a SV30 substrate. The performances of the above mentioned DLC-coatings were surpassed by the recently developed BAM-C (K) coating (Fig. 9) which is based on Zr(C,N) and deposited on SV30 substrates at T~400°C without an intentionally deposited bond layer. This coating with for large batches process optimized, innovative and simplified architecture is slip-rolling resistant at a pres- sure Of Pomean=1.94GPa, independent of the two lubricants used and independent of temperature up to 120°C (coating K1, Fig. 10).

Fig. 9. Slip-rolling resistance of K and N coatings.

Fig. 11. Optical microscopy images of coatings and the respective counterbodies after slip-rolling tests in paraffinic oil, at RT (the bars on the images represent 500 wm. Another advantage of the Zr(C,N) coating is that the parame- ters characterising the mixed and boundary lubrication regime for Summarizing ‘the results of the Zr- based coatings, the slip- rolling tests have proven their tribological suitability for conditions encountered in automotive applications, i.e., for high Hertzian pres- sures and elevated temperature of factory fill oils. In addition, the results obtained for coefficients of friction with hydrocarbon-based

Fig. 12. Optical microscope images of coatings and the respective counterbodies after slip-rolling tests in SAE OW-30, at 120°C. (the bars on the images represent 500 j.m.).

Fig. 13. Optical microscope images of coatings and the respective counterbodies after slip-rolling tests in PBG-B20, at 120°C (the bars on the images represent 500 1m.)

Fig. 15. Topography of the coating I-110n SV30 substrate after test against spherical 100Cr6H (Po mean = 1.75 GPa, FF OW-30, T= 120°C, n= 107 cycles). Fig. 14. Topography of the cylindrical disc SV30 after test against spherical 100Cr6H (Pomean = 1.5 GPa, FF OW-30, T= 120°C, n=107 cycles).

due to the increase in temperature and the respective dilatation of the substrate. Figs. 12 and 13 present the optical microscopy images of some coatings before (a) and after (b) test at 120 °C in SAE OW-30 (Fig. 12) and in PPG (Fig. 13) with their respective counter body (c).

Fig. 16. Planimetric wear of the coating G4 (ta-C) and counterbody 100Cr6 at Po mean = 1.94 GPa, in paraffinic oil, RT, n=2 and 5.5 x 108 cycles.

Viscosimetric properties of lubricants used. eoaanoe Polyalkylene glycol. Phopani. Factory fill oil. Polybutyleneglycol. Polypropylene glycol. Table 2

Hertzian contact pressures for the substrate geometries tested. Table 1

* A.D.: As deposited, c.p.: coating polished again. Selection of industrial coatings systems tested.

Steel substrates used. Table 3 4. Results of the rolling resistance tests

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Computational simulation of model and full scale Class 8 trucks with drag reduction devices

by Kidambi Sreenivas and

2011, Computers & Fluids

Numerical solutions of the unsteady Reynolds-averaged Navier-Stokes equations using a parallel implicit flow solver are given to investigate unsteady aerodynamic flows affecting the fuel economy of Class 8 trucks. Both compressible and... more

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Efficient vehicles versus efficient transportation. Comparing transportation energy conservation strategies

by Todd Litman

2005, Transport Policy

Efficient vehicles, such as this hybrid SUV, consume less fuel per mile but tend to be driven more miles and so increase congestion, road and parking facility costs, crashes and some environmental impacts. This paper investigates these... more

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Projection of energy use and greenhouse gas emissions by motor vehicles in China: Policy options and impacts

by yaodong shi

2012, Energy Policy

We project the well-to-wheels (WTW) and tank-to-wheels (TTW) fossil-energy use, petroleum use, and greenhouse gas (GHG) emissions of the road-transport sector in China up to year 2050 and evaluate the effects of various potential policy... more

Fig. 1. Projected annual TTW oil consumption in China under the six policy scenarios.

Fig. 2. Projected annual TTW oil consumption in China by vehicle type under the BAU policy scenario.

Fig. 3. Projected TTW oil consumption in China by vehicle type under the six policy scenarios: (a) LDVs-low car growth, (b) HDTs, (c) LDTs and (d) HDBs.

Fig. 4. Projected TTW gasoline and diesel consumption in China under the six policy scenarios (assuming the low growth scenario).

Fig. 5. Projected WTW fossil-fuel use in China under the six policy scenarios (assuming the low growth scenario).

Fig. 6. Projected annual TTW and WTW GHG emissions in China under the six policy scenarios (assuming the low-car-growth scenario, except as noted).

Fig. 7. Projected reductions in energy use and GHG emissions in China in the near and long term under four policy scenarios.

Policy packages considered in this study. Table 1

Vehicle-stock levels, VKT, and fuel-consumption rates in China, projected with the FEEI model. * The FEEI model further splits each vehicle category into gasoline-fueled and diesel-fueled. Values in the table represent fleet average values. > Future fuel-consumption rates depend on policy scenarios, which is intro- duced in Section 3 of this article. “ For private LDVs, we examined both “low” and “high” car-growth scenarios, under which the growth trend of private cars in China is assumed to follow the trend of Japan and European countries, respectively (Huo and Wang, this issue).

Projected WTW GHG intensity, oil-use intensity, and coal-use intensity of various fuels. * Share of coal-based electricity in the national power grid is assumed to decrease from 82% in 2010 to 65% in 2030 and 55% in 2050; the combustion efficiency of coal- fired power plants is assumed to increase from 35% in 2010 to 40% in 2030 and 52% in 2050 (Huo et al., 2010). > Share of cellulosic ethanol is assumed to be 10% by 2020, 50% by 2030, and 90% by 2050. The WTW GHG emissions per heating value of starch-based ethanol are assumed to be 20% higher than those of gasoline in 2010 (Ou et al., 2009) and the same as those of gasoline in 2020, equivalent to the level of ethanol plants in the U.S. with 100% coal as process fuel (Wang et al., 2007), and 20% lower in 2050, which is equivalent to the current average U.S. level (Wang et al., 2007). The GHG emissions per heating value of cellulosic ethanol are assumed to be 80% lower than those of gasoline in 2020 and 10% in 2050. The intensity of oil use and coal use is assumed on the basis of other previous studies (Zhang et al., 2008; Ou et al., 2009). “ Energy efficiency of coal-based fuels is assumed to increase from 40% in 2010 to 45% in 2020, 50% in 2030, and 60% in 2050. The penetration of CCS is assumed to be 10% in 2020, 30% in 2030, and 50% in 2050. The CO2-removal efficiency of CCS is assumed to be 85% in 2020, 90% in 2030, and 95% in 2050. The intensities are calculated using the GREET model. 4 TTW GHG emissions of CNG are assumed to be 77% of its WTW GHG emissions, on the basis of the GREET simulation results. © WTW GHG emissions of bio-diesel are assumed to be 10% lower than those of diesel in 2010, 20% lower in 2020, 30% lower in 2030, and 50% lower in 2050, on the

* Blending bio-fuels or coal-based fuels into petroleum fuels. Vehicle types and fuels targeted by various policy options.

Projected market penetration of vehicle/fuel options for private LDVs in China. * Gasoline conventional ICEVs and gasoline hybrids. Fuel-consumption rates of gasoline hybrids are assumed to be 30% lower than those of their conventional counterparts. > Diesel conventional ICEVs and diesel hybrids. Fuel-consumption rates of diesel hybrids are assumed to be 25% lower than those of diesel ICEVs. © Electric vehicles. The fuel-consumption rates of EVs are assumed to be 20 kWh/100 km and 16 kWh/100 km by 2030 under the BAU and fuel-consumption- improvement scenarios, respectively. 4 CNG ICEVs. The gasoline-equivalent fuel-consumption rates of CNG ICEVs were reported to be 8% higher than those of gasoline ICEVs (Wu et al., 2004). At present, most CNGVs are bi-fueled in China. We assume that dedicated CNGVs will be used widely in the future, and the share of dedicated CNGVs will be 100% by 2030. The gasoline-equivalent fuel-consumption rate of dedicated CNGVs is 5% lower than that of gasoline ICEVs (GREET value). © From 2010 to 2050. Table A5

* Limit values for diesel LDVs are 30% lower. > Limit values for diesel LDTs are 22% lower according to the current requirements on diesel and gasoline N1 vehicles (GB 20997-2007). © These groups of vehicles are expected to be 100% diesel-powered in China. Projected fuel-consumption rates (L/100 km) for conventional ICEVs under an improvement scenario based on Japanese and European standards.

Projected share of alternative fuels blended into regular petroleum fuels in China*. * Numbers in table represent national average percentages. > Blended in gasoline. © Blended in diesel. Table A6

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Multivariant design and multiple criteria analysis of building refurbishments

by Behnoosh Akhi

2005, Energy and Buildings

In order to design and realize an efficient building refurbishment, it is necessary to carry out an exhaustive investigation of all solutions that form it. The efficiency level of the considered building's refurbishment depends on a great... more

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Bi-level Dynamic Optimization of Pumped-storage Hydroplants in a Competitive Power Pool. Part I: Model and Algorithm

by Victor Sauchelli

2005, IEEE Latin America Transactions

Resumen--Este trabajo es el segundo de una serie de dos artículos. Presenta los resultados obtenidos mediant simulaciones numéricas con el modelo de optimización dinámica binivel desarrollado para programar la operación diaria de una... more

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Integrated, Feed-Forward Hybrid Electric Vehicle Simulation in SIMULINK and its Use for Power Management Studies

by Dennis Assanis

2001, SAE Technical Paper Series

A hybrid electric vehicle simulation tool (HE-VESIM) has been developed at the Automotive Research Center of the University of Michigan to study the fuel economy potential of hybrid military/civilian trucks. In this paper, the fundamental... more

Figure 2 Hybrid-electric vehicle simulation in SIMULINK converter (TC), whose output shaft is then coupled to the transmission (Trns). The coupling at the transmission output side engages or disengages the electric motor depending on the operation mode of the hybrid. Hence, The paper is arranged as follows. The configuration of the newly developed hybrid electric vehicle system in SIMULINK is discussed first, followed by the description of features of the main simulation modules: diesel engine, drivetrain, vehicle dynamics and_ electric components. Next, two power management algorithms: a rule-based algorithm, and a dynamic programming based optimization algorithm are introduced. The complete hybrid vehicle simulation is then used to assess the acceleration ability and the fuel economy of the hybrid vehicle through comparisons with its conventional counterpart. The two control strategies are evaluated through simulation § predictions of fuel

Figure 1: Schematic of the integrated vehicle system. The vehicle system considered in this work is a 4X2 Class VI truck configured as a parallel hybrid with the electric motor positioned after the transmission. The schematic of the vehicle and the propulsion system is given in Figure 1. The engine is connected to the torque

Figure 4: Engine subsystem in SIMULINK Figure 3: Generating a torque map for a scaled engine

by the IVS (driver) module, environmental conditions and current engine operating conditions, i.e. engine speed and boost pressure. The instantaneous engine speed is provided as the output of the engine dynamics block Figure 4), while the nominal value of boost pressure is abulated as the function of speed and load based on predictions of the high fidelity code. Hence, the part of he fuel control logic that limits the fuel at low boost is retained in a meaningful way. In addition, a carefully calibrated time delay is built-in to represent the effect of urbo-lag on transient response to rapid increases of engine “rack positions’. More details about the engine subsystem and the fuel controller are provided in the work by Assanis et al. [7].

Figure 5: Schematic of vehicle dynamics. The venicie subsystem iInciudes the wheels/tires, axles, suspensions and body of the vehicle. A number of approaches can be used to model vehicle dynamics depending on the overall simulation objectives. A single Degree of Freedom (DOF), point mass model, can be selected for an initial estimate of vehicle performance as different powertrain options are explored. The model complexity can be enhanced with more DOFs as more severe excitations (road roughness, steering, braking, etc.) are introduced into the model. This is necessary for the investigation of vehicle-powertrain interactions during such extreme transients that induce significant pitch motion. The complexity of the model can be systematically adjusted, as proposed by Louca et. al.[15], to accommodate the needs of a_ specific scenario.

Figure 6: Efficiency map of the DC motor — source [9] In addition to introducing extra dynamics, the battery is also important in the overall HE-VESIM fuel economy because of its nonlinear and non-symmetric efficiency characteristics. _ Figure 8 shows the charging and discharging efficiency of the battery. This figure was obtained by reducing the dynamic model to a static one and then computing the instantaneous power efficiency. The process of this efficiency calculation can be found in [18]. It can be clearly seen that, overall, the optimal combined charging-discharging efficiency is obtained around SOC=0.6.

Figure 8: Efficiency maps of the lead acid battery: a) discharging efficiency; b) charging efficiency. POWER MANAGEMENT ALGORITHMS In this section, we will present two power management algorithms: a rule-based algorithm, and a dynamic programming based optimization algorithm. The rule-based algorithm is mainly based on engineering intuition. However, due to the multi-variable nature of HEV control problems, these rules usually fail to capture all the important phenomena. The dynamic programming algorithm can help us understand the deficiency of the rules, and thus makes it possible to construct improved (and more complicated) rules.

Figure 9: Rule based power management will still propel the vehicle to avoid the engine operating in this inefficient region. The battery recharge mode will not stop until the SOC hits the upper bound SOC ya, (60%). max

Figure11: Critical system variables during 0-60 mph acceleration: conventional truck (VESIM) vs. the hybrid electric truck (HE-VESIM) examine more closely how one of the control algorithms, e.g. the rule-based controller, switches between different modes of hybrid operation during a driving schedule. For that purpose, a close-up of the 430 to 540 second period of the driving cycle is given in Figure 13. This segment of the cycle includes two acceleration—cruising— deceleration profiles (see Figure 13a), the first one requiring harder acceleration to higher speed. The battery SOC, engine power and electric motor power are given in Figures 13b, c and d, respectively. The truck launches from stop using only the motor to avoid inefficient engine operation under low power demands. However, the engine is turned on very quickly, since the power demand requires the output from both the engine and the electric motor. From 450 to 477 sec, the power required to cruise at the speed of 35 mph is less than the “engine on” power level, hence the engine is disengaged and the motor supplies all the torque required at the wheels. When the truck decelerates, the regenerative braking is applied; hence the motor operates as a generator to recover the energy that would otherwise be dissipated in brakes (477 to 490 sec and 530 to 537 sec interval). It should be noted that when the battery SOC hits the lower bound (55%), at 523 seconds into the schedule, the engine is immediately turned on to power the truck, as well as to recharge the battery. Hence, the electric motor is switched to the generator mode and its torque becomes negative.

are identical. This is not surprising since the braking strategies of the two algorithms are identical. Besides, both algorithms use the motor during launch to avoid inefficient engine operation. Since the rule-based algorithm is in the charging mode within this time period, Furthermore, a close-up examination of the behavior of two vehicles with different control algorithm between 690 to 765 second of the driving cycle is given in Figure 14. The behaviors of the two vehicles under deceleration (from 714 to 726 sec, and 749 to 763 sec)

Figure 14: The close-up of the 690 — 765 second interval during the urban driving cycle: comparison between the Dynamic Programming (DP) and Rule based (RB) control. The fuel economy results over the complete urban driving schedule, obtained for the two control algorithms are compared with a conventional diesel engine truck in Table 1. These results are obtained for the charge sustaining strategy, with the SOC at the end of the cycle being the same as it was at the beginning. It can be seen that the fuel economy improvement (over the conventional truck) is about 22% for the rule-based algorithm and 33% for the dynamic-programming algorithm. For the case of rule-based algorithm, a large portion of its improvement is due to regenerative braking Further improvement was obtained by dynamic programming through better-orchestrated coordination between the operation of engine/transmission and motor/battery. Figure 15 shows engine operating points on the BSFC map calculated during the driving schedule

Table 1 Fuel consumption comparison: conventional, dynamic programming (DP), rule-based (RB) for all three vehicle configurations (conventional, HEV- rule based and HEV-dynamic programming). The position of the largest clusters of points on both HEV maps is much more favorable compared to the conventional vehicle, i.e. the engine is forced to operate at relatively higher loads and points are moved closer to the high efficiency region. Closer examination of the maps obtained for the two HEV versions indicates that, the rule-based algorithm has achieved a more consistent engine operation near the island of optimum efficiency, but quite a few points are located on the maximum torque line. The dynamic programming algorithm produces higher overall fuel economy by exploring the efficiency of the whole system, instead of focusing just on the engine efficiency. In other words, it appears that in some instances it is more effective to sacrifice some of the engine efficiency and gain on other fronts, through more efficient motor operation and better-optimized charging/discharging schedule. This observation is currently being analyzed in more detail in order to devise an improved rule-based algorithm for the HE-VESIM.

Table 2: Drivetrain Specifications Table 1: DI Diesel Engine Specification

Table 3: Vehicle Dynamics Specifications

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Control System Development for an Advanced-Technology Medium-Duty Hybrid Electric Truck

by Jason Liu

2003

The power management control system development and vehicle test results for a medium-duty hybrid electric truck are reported in this paper. The design procedure adopted is a model-based approach, and was based on the dynamic programming... more

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Modeling and Validation of a Fuel Cell Hybrid Vehicle

by Keith Wipke

This paper describes the design and construction of a fuel cell hybrid electric vehicle based on the conversion of a five passenger production sedan. The vehicle uses a relatively small fuel cell stack to provide average power demands,... more

Hybrid electric vehicles (HEV’s) combine the benefits ot several propulsion components in an attempt to produce a more efficient vehicle. A common approach to hybrid vehicle design takes a conventional vehicle drivetrain and combines it with components commonly found in an electric vehicle. In this type of vehicle, a gasoline engine might be augmented by an electric motor. In a fuel cel hybrid a different approach must be taken to harness the

Figure 4. Polarization Curve, manufacturer vs. FC-HEV Fuel cell stack performance testing was completed by Energy Partners (EP) before shipping and was verified by system tests and on-vehicle testing (IN HEV). (See Figure 4)

Figure 5. Air Compression System Power Flow improved fuel cell performance over 10 psig operation, the overall system efficiency was lowered due to the high parasitic energy demand. Because of this, the operating pressure of the stack was chosen to be 70 kPa (10 psig), allowing for more efficient operation overall.

Figure 6. Accessory Load Block lumped into an accessory power block (Figure 6) in addition to existing vehicle accessories

Figure 7. Boost Converter Power Transfer Curve A component unique to the design of the Virginia Tech Fuel Cell Hybrid Electric Vehicle is a power electronic device know as a boost converter. The boost converter used in this design serves two important purposes in the Virginia Tech Fuel Cell Hybrid Electric Vehicle (VT FC- HEV). First, it boosts the low voltage output of the fuel cell to match the voltage of the vehicle’s battery pack, which varies widely with state of charge (SOC) and vehicle load. Secondly, it incorporates a load following portion of the vehicle control strategy to the fuel cell system, supplying a higher power level to the vehicle when it is under higher load. By sensing battery pack voltage, the boost converter allows the fuel cell to operate efficiently at low power when the vehicle sees light loads, while still supplying it with adequate power to maintain adequate SOC when vehicle power demand is high or to return power to the battery pack when it is discharged. Figure 7 shows a plot of this load following function.

Power Curves for the Boost Converter were produced from 2 test runs and are displayed with in vehicle test data in Figure 7. The first test was relatively noisy data Since the fuel cell’s open circuit voltage is 110V, operation by direct connection to the battery pack was not an option. The boost converter provided a way to boost the power of the fuel cell up to the voltage of the

provided by the manufacturer of the unit. In an effor to verify the accuracy of this data and to gain a better understanding of how the device would function in the a hybrid vehicle a test session was arranged on an AB 150 controlled DC power source that would simulate C- he operation of the fuel cell, the boost converter was tested to approximately 80% of full power, limited by maximum current available from the ABC-150. An attempt model the fuel cell polarization curve was implemen to ed on the ABC-150 by using a straight-line approximation. This approximation was coded into a script file that varied the ABC-150’s voltage levels to match 138k (20psi) fuel cell performance data collected by Ener Partners. However, the ABC-150 was slow to respo to this script file and it could not properly simulate t Pa gy nd ne fuel cell’s power curve at light load. Despite limitations, the results of this test were good, both in verifying t ne manufacturers data and in learning more about t operating characteristics of the Boost Converter. ne ~60V fuel cell to the ~330V battery pack. When the system is operating, this block transfers power to the Power Buss at a rate controlled by a 1-D lookup table based on in-vehicle testing. The input to this table is the calculated battery pack voltage of the Energy Storage System (ESS) block to emulate the behavior of the battery pack in the actual vehicle.

The model shown in Figure 8 recreates the action of the Boost Converter, the device that boosts power from the To better model the Virginia Tech fuel cell hybrid the series hybrid block diagram is altered to contain the proper components blocks and data files are loaded with information from the test phase of the project. This

Vehicle testing was performed at an emissions test facility equipped with roller dynamometers capable of performing a range of tests including the Federal Urban Driving Schedule (FUDS) and the Highway Fuel Economy Test (HWFET). See Figure 11. The VT FC- HEV completed each of these tests running on hydrogen as a fuel cell hybrid vehicle.

Figure 13. Highway Cycle — Drive Motor Power

Figure 14. City Cycle — Fuel Cell System Net Power

Figure 15. Highway Cycle - Fuel Cell System Net Power

Figure 16. City Cycle — Battery Amp Hours Used

Figure 18. City Cycle - Battery Buss Voltage Tracking

Figure 19. Highway Cycle - Buss Voltage Tracking

Figure 21. City Test Data- Electric Generation Eff. As seen in Figures 21 and 22, peak fuel cell system efficiency of the Virginia Tech fuel cell system is comparable with that of conventional energy generation systems and is somewhat less than what has recently been made possible in lightweight diesel and Dl-gasoline engines. Although fuel cells, at first glance, have amazing efficiency potential, the engineering challenge is not trivial when all aspects of operating a fuel cell system are considered. Poor attention to detail can quickly yield a system that barely produces any net power at all. However, one must consider that this particular fuel cell system was built completely by undergraduate students with no prior experience with fuel cells and little more than a list of guidelines to start with. Their achievement is an impressive showing of ingenuity and persistence. Given only 8 months to complete their task, there is much room for improvement in the control and operation of the system itself and in the integration of the system into the Chevrolet Lumina chassis. Items for improvement include weight reduction, air compressor load following as discussed above, and control strategy optimization.

As Shown in Figures 23 and 24, Total electric energy use and generation is very close between model and testing great proving the validity of the vehicle model and boost converter modeling technique. Overall fuel use is slightly off the highway driving cycle but was very accurate on the city cycle. This is due in part to the fact that the data used to predict fuel use was averaged over the both the city and highway cycles. Because the city cycle is nearly twice as long as the highway cycle, the fuel use during this period therefore has a greater impac on the averaged data than the highway cycle. Other factors that include thermal issues, since the fuel cel system was fully warmed up on the highway cycle and performed differently than when it was after a cold-star during the city cycle. The model did not incorporate thermal compensation and could not simulate this effec in final fuel economy numbers.

Figure 26. Generic Block Diagram with Boost Converter This new model of a fuel cell system includes avenues to ease entry of new fuel cell data, and even Figure 27 compares the 1999 FutureCar Challenge data o the “Generic Model” results. However, rather than use the standard series power follower control strategy ound in ADVISOR, a “boost converter style” of power request, based on buss voltage, was used to command he power level of the fuel cell system block. The ollowing power history plot (Figure 27) shows that this The VT FC-HEV ADVISOR model provides a reasonably accurate model of the vehicle and the interconnection o vehicle and the fuel cell systems that are specific to the Virginia Tech design. Its disadvantages are that i requires a very accurate model of the vehicle’s energy storage system and load-following power transfer system. This model is not versatile enough in its curren form to be released into the ADVISOR environment for public usage. Therefore, a more generalized model was developed following the proven themes used in the initia model. This new “generic” model individually incorporates the behaviors of the fuel cell stack, reactan supply systems, and cooling systems to form a more integrated, thorough model.

incorporates a thermal model to help approximate the affect of cold starts on fuel consumption of a fuel cell stack. Work continues with NREL to complete the integration of this model into a release version of ADVISOR. Further work also includes modeling of the VT FC-HEV in its as-designed state with all systems functioning at full capability. This model is expected to show better fuel economy as well as be charge sustaining over the EPA driving cycles. functioning at full power, the energy storage system would have remained nearly charged and the majority of the power would have been generated directly from hydrogen.

Figure 27. City Cycle - Fuel Cell System Power Tracking new model, combined with the boost converter control methodology, functions accurately. Using this method, the power tracking and energy use for the generic fuel cell system much more closely resembled the test data.

Future 28. Energy Use - new ‘generic’ fuel cell model Current status / Conclusions

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Design Optimization of a Parallel Hybrid Electric Powertrain

by Sachin Kumar

2005, Vehicle Power and Propulsion, 2005 …

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Analytical Approach for the Power Management of Blended-Mode Plug-In Hybrid Electric Vehicles

by Chris C Mi

2000, IEEE Transactions on Vehicular Technology

This paper focuses on the intrinsic aspects of power management for hybrid electric vehicles (HEVs) and plug-in HEVs (PHEV). A vehicle power distribution density function is used to describe the drive cycle characteristics. An electric... more

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Engine-in-the-Loop Testing for Evaluating Hybrid Propulsion Concepts and Transient Emissions - HMMWV Case Study

by Dennis Assanis

2006, SAE Technical Paper Series

This paper describes a test cell setup for concurrent running of a real engine and a vehicle system simulation, and its use for evaluating engine performance when integrated with a conventional and a hybrid electric driveline/vehicle.... more

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Fuel economy and emissions evaluation of BMW Hydrogen 7 Mono-Fuel demonstration vehicles

by Thomas Wallner and

2008, International Journal of Hydrogen Energy

Efficiency Emissions a b s t r a c t This article summarizes the testing of two BMW Hydrogen 7 Mono-Fuel demonstration vehicles at Argonne National Laboratory's Advanced Powertrain Research Facility (APRF). The BMW Hydrogen 7 Mono-Fuel... more

Fig. 3 - Schematic of the catalyst setup of the BMW Hydrogen 7 Mono-Fuel demonstration vehicles [8]. Lean mixtures with a relative air/fuel ratio (A) in excess of A = 2 can be burned without generating NO, emissions. Below this relative air/fuel ratio of A= 2 the nitric oxide emissions increase exponentially with a maximum around 4 = 1.3. When approach- ing stoichiometric mixtures the raw NO, emissions decrease.

Nitric oxide (NO,) emissions occur in combustion engines due to the high temperatures in combination with the presence of nitrogen (N2) from air. NO, emissions are the only critical emissions component for hydrogen combustion engines. For homogeneous operation the amount of nitric oxide emissions created during combustion of hydrogen is exponentially dependent on the composition of the fuel air mixture.

Fig. 4 - Schematic of engine operating strategy implemented in the BMW Hydrogen 7 vehicles [11] The safety system is designed to handle advanced hybrid or alternative fuel vehicles. Gas detectors, as well as heat and smoke detectors are linked to a sprinkler system, air handling unit and the fire department. An emergency stop system with manual trigger switches and sensor triggers is integrated into the facility.

Fig. 5 - Schematic of Argonne’s Advanced Powertrain Research Facility (APRF) [12].

Fig. 7 - Typical humidity levels of the diluted exhaust during a FTP-75 test. The first four measures are required to allow accurate determination of the water content in the exhaust. A water analyzer was integrated into the emissions sampling system to allow for that measurement. In order to stay below the maximum range of the water analyzer (5 Vol-%) a high dilu- tion rate was necessary. This required all tests to be run at CVS flow rates of approximately 24Nm*/min. These high flow rates in combination with heated lines all the way from the tailpipe to the sampling point reduce the chance of water condensation in the exhaust stream. The effectiveness of these measures can be seen in Fig. 7 showing the speed trace as well as the trace of water concentration of the diluted

Fig. 6 - Deer antlers connected to BMW Hydrogen 7 in vehicle soak area.

Fig. 9 - Schematic of setup for validation of water balance for fuel consumption measurement on hydrogen vehicles.

Fig. 8 - Influence of increased water content on CO and HC analyzer readings.

Fig. 10 — Correlation between fuel consumption measurement methods for steady-state operating points.

The impact of the changes in relative air/fuel ratio on the nitric oxide emissions is shown in Fig. 15. The trace shows the modal NO, readings of the diluted exhaust stream during a FTP-75 cold-start test. During most of the test cycle the emissions trace actually matches the zero-line. Four phases during the entire test are relevant for the NO, emissions measurement. These are the two engine starts as well as the two fast accelerations during the second hill of the 505 cycles. The first engine start shows slightly higher spikes than the second one, which might be due to the cold catalyst during the

Fig. 14 — Relative air/fuel ratio during FTP-75 test.

Fig. 13 - Emissions signature when sampling ambient air through an imaginary vehicle. The modal diluted total hydrocarbon (THC) emissions are plotted in Fig. 16. The horizontal section during the 10 min soak shows the background level of THC emissions in the dilution air. The BMW Hydrogen 7 vehicle is capable of actively reducing the THC concentration as can be seen by the fact that the average curve during engine operation is lower than the one during the soak phase. The most interesting phases during vehicle operation are again the engine starts as well as the switch-over from lean to stoichiometric operation. During the first engine start the THC emissions decrease from an increased level. This is likely due to the catalyst warm-up. The second engine start only shows a minor peak during that phase. Both switching phases from lean to stoichiometric operation show a distinct THC emissions peak. Unlike NO, emissions that can only be converted at stoichiometric oper- ation, the oxidation of hydrocarbons in the catalyst occurs

Fig. 12 - BMW Hydrogen 7 emissions results compared to SULEV limits.

Fig. 15 - Modal nitric oxide (NO,) emissions during FTP-7! test. Fig. 17 shows the modal results of methane (CH) emis- sions. As can be seen from the 10 min soak, the background concentration of methane is approximately 1.8 ppm. Similar to the total hydrocarbon emissions the methane traces during

Fig. 18 - Modal hydrogen (H2) emissions during FTP-75 test.

Fig. 16 - Modal total hydrocarbon (THC) emissions during FTP-75 test.

Fig. 19 - Modal carbon monoxide (CO) emissions during FTP-75 test. The increased readings of CO during engine operation are very likely also only due to the water cross-sensitivity of the analyzer. As shown in Fig. 8 the CO analyzer shows about 0.5 ppm of carbon monoxide (CO) per percent of water in the

Fig. 17 - Modal methane (CH) emissions during FTP-75 test. Both, the peaks in total hydrocarbons as well as methane are significantly higher than the ambient concentration of the respective concentration. Therefore the peaks cannot just be attributed to a lack of emissions conversion efficiency of the catalyst. The flame ionization detectors used for measuring hydrocarbon emissions are sensitive to changes in humidity. The sudden increase in humidity caused by switching from lean to stoichiometric operation could increase the hydro- carbon readings (see [17]). Flame ionization detectors use hydrogen as a burner gas. A sudden increase in hydrogen concentration as seen during the transition from lean to stoichiometric operation (see Fig. 18) could also cause increased hydrocarbon readings. There are several other possible causes for the HC peaks including for example actual

Introduction manufacturing and quality control process as any other BMW vehicle. The new hydrogen model is built at BMW’s Dingolfing Plant (Germany) parallel to the other models in the BMW 7, 6 and 5 Series, with the drive unit in BMW Hydrogen 7 coming like all BMW twelve-cylinder engines from the Company’s engine production plant in Munich (Germany).

Table 3 - Summary of BMW Hydrogen 7 fuel economy numbers

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Modelling and control strategy development for fuel cell electric vehicles

by doanh tran

2005, Annual Reviews in Control

This paper describes a modelling and dynamic control design process applicable to the development of fuel cell electric vehicles (FCEVs) and hybrid electric vehicles (HEVs). After an introduction to advanced propulsion technologies the... more

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The influence of Exhaust Gas Recirculation (EGR) on combustion and emissions of n-heptane/natural gas fueled Homogeneous Charge Compression Ignition (HCCI) engines

by David Checkel

2011, Applied Energy

In Homogeneous Charge Compression Ignition (HCCI) combustion, a lean premixed charge combusts simultaneously in multiple sites. Utilizing highly diluted mixtures, and lack of any significant flame propagation, in-cylinder NO x formation... more

Fig. 1a. Calculated in-cylinder mean charge temperature for data set I.

Fig. 2a. Averaged measured in-cylinder pressure traces for data set I. Gross HRR for two mentioned data sets are shown in Fig. 4. As can be seen, applying EGR has profound effect on HRR magnitude and phasing. Recycled exhaust gases substitute part of fresh intake, and as a result affect in-cylinder equivalence ratio, and conse-

Fig. 1b. Calculated in-cylinder mean charge temperature for data set II.

Fig. 4a. The effect of EGR on gross heat release rate for data set I. Fig. 3b. Calculated convective wall heat transfer rate for data set II.

Fig. 2b. Averaged measured in-cylinder pressure traces for data set II.

Fig. 3a. Calculated convective wall heat transfer rate for data set I.

Fig. 4b. The effect of EGR on gross heat release rate for data set II.

Fig. 5a. The influence of EGR on Start of Combustion.

Fig. 5b. The influence of EGR on combustion duration.

Fig. 7. The influence of EGR on indicated specific fuel consumption.

Fig. 8a. The influence of EGR on emission characteristics of NOx.

Fig. 8b. The influence of EGR on emission characteristics of HC.

Fig. 8c. The influence of EGR on emission characteristics of CO.

CFR engine operating conditions for data set I. Table 2a

CFR engine operating conditions for data set II. Table 2b a solver was developed for calculating the mean charge tempera- ture [14].

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A review of regulatory instruments to control environmental externalities from the transport sector

by Hari Dulal

2009

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The influence of H2H2 and CO on diesel engine combustion characteristics, exhaust gas emissions, and after treatment selective catalytic NOxNOx reduction

by Thanos Megaritis

2007, International Journal of Hydrogen Energy

The requirement to significantly reduce NO x and particulate matter (PM) emissions while maintaining efficient combustion performance is one of the main drivers for internal combustion engine research. Modern diesel and premixed charge... more

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Performance and emission characteristics of LPG powered four stroke SI engine under variable stroke length and compression ratio

by Jehad Yamin

2008, Energy Conversion and Management

A computer simulation of a variable stroke length, LPG fuelled, four stroke, single cylinder, water cooled spark ignition engine was done. The engine capacity was varied by varying the stroke length of the engine, which also changed its... more

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Driving/Regeneration and Stability Driver Assist In 4WD Hybrid Vehicles

by Reza Hoseinnezhad

2008, … , 2008. AUPEC'08. …

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Nonlinear air-to-fuel ratio and engine speed control for hybrid vehicles

by John Wagner

2003, IEEE Transactions on Vehicular Technology

Internal combustion spark ignition engine management systems regulate the fuel, spark, and idle air subsystems to achieve sufficient engine performance at acceptable fuel economy and tailpipe emission levels. Engine control units also... more

Fig. 2. ECU and internal combustion engine schematic.

Fig. 3. Parallel hybrid vehicle (rear wheel drive) with internal combustion spark ignition engine driving a CVT and alternator/generator.

Fig. 4. (a) Torque versus speed efficiency map for 1.0-L SI engine with automatic. (b) 1.0-L engine torque versus speed map for CVT with efficiency contours.

Fig. 5. (a) Engine speed PID and air-to-fuel ratio PI controller results for constant load torque (case one) with air-to-fuel ratio tracking within an 8.0% neighborhood of stoichiometric. (b) Engine speed PID and air-to-fuel ratio PI controller results for a load torque disturbance (case two) with air-to-fuel ratio departures at the transition points.

Fig. 6. (a) Backstepping controller results for constant load torque (case one); the air-to-fuel ratio tracks within a 4.0% neighborhood of stoichiometric value. (b) Backstepping controller engine speed and air-to-fuel ratio results for a load torque disturbance between 6 and 8 s (case two).

Fig. 7. (a) Sliding-mode controller results for constant load torque (case one) with an air-to-fuel ratio within 6.0% stoichiometric value. (b) Sliding-mode controller results for load torque disturbance (case two) with engine speed (rpm) and air-to-fuel ratio versus time (seconds).

LISTING OF THE PARAMETERS FOR THE MoDEL- FREE AND MODEL-BASED CONTROLLERS A suite of subsystems was created in Matlab/Simulink ! to implement the three engine control strategies. The controller parameters are summarized in Table I. These subsystems were then integrated into an existing analytical and empirical engine model [30], which describes the thro manifold, combustion process, and rotational ttle body, intake dynamics for an internal combustion spark ignition utility engine. An integra- tion time-step of At = 0.01 s and fourth-order Runga- Kutta integration routine were selected. The average engine speed shall be N = 2100 rpm with a sinusoida amplitude AN = 100 rpm and frequency f disturbance of = 1.0 Hz such that V,, = N + ANsin (27 f)t. Two load torque cases were examined. Case one denotes a constant load torque, and case two explores a 33% torque step increase for 6.0 < ¢t < 8.0S. Fi- nally, the air-to-fuel ratio shall be maintained at stoichiometric.

SUMMARY OF ENGINE CONTROLLER PERFORMANCE to the disturbance rejection attributes of the controller. Overall, the backstepping controller demonstrates good robustness to external disturbances and signal noise. However, in the presence of model uncertainty and/or parameter variations, the controller performance will deteriorate since the underlying model no onger corresponds to the actual plant. Consequentially, an adaptive controller may need to be designed to compensate for system uncertainty through the online estimation of the uncertain parameters. in Fig. 7(b). The engine speed does not significantly deviate (i.e., 0.9%) from desired speed N.,,. The air-to-fuel ratio has increased “chattering” during the disturbance time interval; however, the magnitude of deviation from stoichiometric is approximately the same as case one. With a load disturbance, the sliding-mode controller performance deteriorated 2.94% and 0.62% for the summed air-to-fuel ratio and engine speed errors f |e,|dt and f |ex|dt per Table II, respectively. In con- trast, the backstepping controller displayed error magnitudes of 13.04% and 1.68% for these same metrics. The disturbance rejection qualities for the sliding-mode controller are superior to the backstepping controller.

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