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Figure 2: Dual UEGO Fore-Aft Controller A review of electrochemical sensor technology may be found in [33]. Control and diagnosis of catalysts using UEGO sensors are described by [84, 35]. In [36, 37], Fiengo and co-authors use the catalyst model described above along with pre- and post-catalyst UEGO sensors to develop a controller with two objectives: to simultaneously maximize the conversion efficiencies of HC, CO and NO,, and to obtain steady-state air- fuel control that is robust with respect to disturbances. A series controller topology is adopted as illustrated in Figure 2. The objective of the first block, the Fore Controller, is to respond relatively quickly to A/F disturbances on the basis of measured feedgas oxygen level. The objective of the second block, the Aft Controller, is to adjust the setpoint of the fore controller, on the basis of both A/F measurements, so that the TWC achieves simultaneously high conversion efficiencies for HC’ and NO,. The aft controller is composed of a bias estimator and a proportional term. The bias estimator uses the upstream and downstream A/F measurements to correct the error in the upstream oxygen sensor. The proportional controller feeds back the post-catalyst UEGO sensor measurement and establishes the reference for the fore controller.

Figure 2 Dual UEGO Fore-Aft Controller A review of electrochemical sensor technology may be found in [33]. Control and diagnosis of catalysts using UEGO sensors are described by [84, 35]. In [36, 37], Fiengo and co-authors use the catalyst model described above along with pre- and post-catalyst UEGO sensors to develop a controller with two objectives: to simultaneously maximize the conversion efficiencies of HC, CO and NO,, and to obtain steady-state air- fuel control that is robust with respect to disturbances. A series controller topology is adopted as illustrated in Figure 2. The objective of the first block, the Fore Controller, is to respond relatively quickly to A/F disturbances on the basis of measured feedgas oxygen level. The objective of the second block, the Aft Controller, is to adjust the setpoint of the fore controller, on the basis of both A/F measurements, so that the TWC achieves simultaneously high conversion efficiencies for HC’ and NO,. The aft controller is composed of a bias estimator and a proportional term. The bias estimator uses the upstream and downstream A/F measurements to correct the error in the upstream oxygen sensor. The proportional controller feeds back the post-catalyst UEGO sensor measurement and establishes the reference for the fore controller.

Related Figures (15)

Figure 1: TWC conversion efficiency versus A/F
Figure 1: TWC conversion efficiency versus A/F
Figure 3: Engine model with VCT and electronic throttle which, for the design model of [42], is approximated by a function affine in P;:
Figure 3: Engine model with VCT and electronic throttle which, for the design model of [42], is approximated by a function affine in P;:
Figure 4: Torque response of the VCT engine to cam phasing steps with and without compensatior
Figure 4: Torque response of the VCT engine to cam phasing steps with and without compensatior
Figure 5: Schematic diagram of a turbocharged gasoline engine. turbocharger, with wastegate and an intercooler. A schematic diagram of a turbocharged gasoline engine is shown in Figure 5. The representation of the turbocharger consists of models of the compressor, turbine and wastegate, and includes the dynamic coupling of the compressor and turbine. The mass flow rate through the compressor, W,, is described by
Figure 5: Schematic diagram of a turbocharged gasoline engine. turbocharger, with wastegate and an intercooler. A schematic diagram of a turbocharged gasoline engine is shown in Figure 5. The representation of the turbocharger consists of models of the compressor, turbine and wastegate, and includes the dynamic coupling of the compressor and turbine. The mass flow rate through the compressor, W,, is described by
Figure 6: Block diagram of decentralized boost control LO ACCOULLL LOL LEMPCLAbvULe GYHales ANQ/OL Meal tialist€l |v, Jl}, GEPCUalis Ol LHe appiearlol,. A turbocharged system model of this type is used by the authors of [46] to analyze system characteris- tics and develop charge control algorithms for a wastegated turbocharged system equipped with electronic throttle. Boost pressure and intake manifold pressure are both measured and conventional decentralized PI] control with feedforward on the wastegate is used to regulate these measured variables to desired setpoints. which are chosen to achieve fuel economy, emissions and driveability objectives. The control structure is shown in Figure 6. This approach produces acceptable performance, however the wastegate is prone to saturation. Multi- variable control techniques can be used to analyze the system to guide formulation of a modified controller that maintains a simple structure desirable for implementation, and yet benefits from a centralized control methodology. Such an approach is described in [58]. This technique reformulates full state feedback integral control as an output feedback control by
Figure 6: Block diagram of decentralized boost control LO ACCOULLL LOL LEMPCLAbvULe GYHales ANQ/OL Meal tialist€l |v, Jl}, GEPCUalis Ol LHe appiearlol,. A turbocharged system model of this type is used by the authors of [46] to analyze system characteris- tics and develop charge control algorithms for a wastegated turbocharged system equipped with electronic throttle. Boost pressure and intake manifold pressure are both measured and conventional decentralized PI] control with feedforward on the wastegate is used to regulate these measured variables to desired setpoints. which are chosen to achieve fuel economy, emissions and driveability objectives. The control structure is shown in Figure 6. This approach produces acceptable performance, however the wastegate is prone to saturation. Multi- variable control techniques can be used to analyze the system to guide formulation of a modified controller that maintains a simple structure desirable for implementation, and yet benefits from a centralized control methodology. Such an approach is described in [58]. This technique reformulates full state feedback integral control as an output feedback control by
Figure 7: Block diagram of DISC engine model References [60, 61] describe modeling and control of a direct injection stratified charge (DISC) gasoline engine and discuss the fundamentally hybrid nature of the system. This model is illustrated in Figure 7. On the surface, the model structure is not dissimilar to a conventional PFI engine discussed in Section 2, consisting of the throttle, intake manifold dynamics, engine pumping, torque generation, rotational inertia and feedgas emissions. In fact, many of the equations used to describe the PFI engines in Section 2 can be applied here. Because of the different characteristics for homogeneous and stratified operation, the model is, in fact, hybrid in the sense that most components are represented by two continuous-variable sub-models with a discrete switching mechanism to select the appropriate characterization based on injection timing. Additionally, the injection-to-torque delay, fundamentally associated with the four-stroke engine cycle (intake-compression- power-exhaust), becomes a function not only of engine speed, but also of the operating mode that dictates the relationship between the injection and combustion events.
Figure 7: Block diagram of DISC engine model References [60, 61] describe modeling and control of a direct injection stratified charge (DISC) gasoline engine and discuss the fundamentally hybrid nature of the system. This model is illustrated in Figure 7. On the surface, the model structure is not dissimilar to a conventional PFI engine discussed in Section 2, consisting of the throttle, intake manifold dynamics, engine pumping, torque generation, rotational inertia and feedgas emissions. In fact, many of the equations used to describe the PFI engines in Section 2 can be applied here. Because of the different characteristics for homogeneous and stratified operation, the model is, in fact, hybrid in the sense that most components are represented by two continuous-variable sub-models with a discrete switching mechanism to select the appropriate characterization based on injection timing. Additionally, the injection-to-torque delay, fundamentally associated with the four-stroke engine cycle (intake-compression- power-exhaust), becomes a function not only of engine speed, but also of the operating mode that dictates the relationship between the injection and combustion events.
Other constraints on the minimization include manifold pressure (sufficient vacuum must be maintained to operate vacuum controlled devices such as the power brake booster), the limits of authority of the throttle and EGR valve, and allowable spark timing and A/F. Figure 9 shows typical A/F and torque traces on a small DISC engine for constant torque combustion mode transitions. In the case of a transition from homogeneous to stratified, the transient A/F requirement is relaxed, giving the fuel actuator substantial authority to maintain constant torque during the mode shift. On the other hand, the transition from stratified to homogeneous operation at stoichiometry requires tight control on A/F to meet emission requirements. Consequently, 7; is large, requiring torque management via spark, which has limited authority, and throttle, which is slow acting, resulting in slightly deteriorated control. Figure 9: Constant torque DISC mode transition on an engine dynamometer. Homogeneous to stratified transition (left) prioritizes torque control; stratified to homogeneous transition (right) relaxes the torque objective to ensure A/F control at stoichiometry
Other constraints on the minimization include manifold pressure (sufficient vacuum must be maintained to operate vacuum controlled devices such as the power brake booster), the limits of authority of the throttle and EGR valve, and allowable spark timing and A/F. Figure 9 shows typical A/F and torque traces on a small DISC engine for constant torque combustion mode transitions. In the case of a transition from homogeneous to stratified, the transient A/F requirement is relaxed, giving the fuel actuator substantial authority to maintain constant torque during the mode shift. On the other hand, the transition from stratified to homogeneous operation at stoichiometry requires tight control on A/F to meet emission requirements. Consequently, 7; is large, requiring torque management via spark, which has limited authority, and throttle, which is slow acting, resulting in slightly deteriorated control. Figure 9: Constant torque DISC mode transition on an engine dynamometer. Homogeneous to stratified transition (left) prioritizes torque control; stratified to homogeneous transition (right) relaxes the torque objective to ensure A/F control at stoichiometry
Using dynamic programming, the authors of [81] also explore the benefits of air-fuel ratio profiling i1 achieving improved fuel economy, NO, and HC emissions tradeoffs. By allowing A/F to vary during th purge phase, they show that substantial leverage can be achieved in reducing HC’ and NO, emissions without a negative impact on fuel economy. Figure 10: Fuel economy versus NO, emissions of optimal policy with calibrations and full optimization over the Euro-cycle. The DISI engine and aftertreatment models are quasi-static. The LNT NO, filling and emptying is dynamically updated.
Using dynamic programming, the authors of [81] also explore the benefits of air-fuel ratio profiling i1 achieving improved fuel economy, NO, and HC emissions tradeoffs. By allowing A/F to vary during th purge phase, they show that substantial leverage can be achieved in reducing HC’ and NO, emissions without a negative impact on fuel economy. Figure 10: Fuel economy versus NO, emissions of optimal policy with calibrations and full optimization over the Euro-cycle. The DISI engine and aftertreatment models are quasi-static. The LNT NO, filling and emptying is dynamically updated.
Figure 11: Steady-state dependence of compressor mass air flow, W.1, on VGT position, xy for different positions of the EGR valve, Yegr.
Figure 11: Steady-state dependence of compressor mass air flow, W.1, on VGT position, xy for different positions of the EGR valve, Yegr.
Figure 12: Fuel cell system diagram and its main auxiliary components A schematic diagram of a fuel cell system and its main auxiliary components is shown in Figure 12. The main subsystems include the fuel cell stack, hydrogen and air supply systems, cooling system, humidification system, and the power conditioning system. Many fuel cell control problems have been discussed in [124, 125]. In the subsequent discussion, we will highlight the key features of the control oriented fuel cell models, the main control problems and the characteristics of the associated solutions.
Figure 12: Fuel cell system diagram and its main auxiliary components A schematic diagram of a fuel cell system and its main auxiliary components is shown in Figure 12. The main subsystems include the fuel cell stack, hydrogen and air supply systems, cooling system, humidification system, and the power conditioning system. Many fuel cell control problems have been discussed in [124, 125]. In the subsequent discussion, we will highlight the key features of the control oriented fuel cell models, the main control problems and the characteristics of the associated solutions.
Figure 13: Three types of hybrid electric vehicles. When fuel economy is the main design goal, as a general rule of thumb, a driving environment with lower average speed and frequent acceleration/deceleration is likely to see higher improvement. Larger vehicles (e.g., a large SUV) will probably see larger and faster market penetration, compared with smaller vehicles, because of their more favorable fuel saving returns.
Figure 13: Three types of hybrid electric vehicles. When fuel economy is the main design goal, as a general rule of thumb, a driving environment with lower average speed and frequent acceleration/deceleration is likely to see higher improvement. Larger vehicles (e.g., a large SUV) will probably see larger and faster market penetration, compared with smaller vehicles, because of their more favorable fuel saving returns.
Figure 14: Schematic of a Hybrid-Electric Truck. where u(k) is the vector of control variables such as desired output power from the engine, desired output power from the motor, and gear shift command to the transmission and x(k) is the state vector of the system The optimization goal is to find a charge-sustaining control policy that minimizes a weighted sum of fue consumption and emissions over a given driving cycle
Figure 14: Schematic of a Hybrid-Electric Truck. where u(k) is the vector of control variables such as desired output power from the engine, desired output power from the motor, and gear shift command to the transmission and x(k) is the state vector of the system The optimization goal is to find a charge-sustaining control policy that minimizes a weighted sum of fue consumption and emissions over a given driving cycle
Figure 15: An indirect feedback design and evaluation process using deterministic dynamic optimization As an illustration of how rules are extracted from u*(«,k), define the power split ratio (PSR) as PSR = Peng/Preq, Which can be used to quantify the positive power flows in the powertrain, where P.,,g is the engine power and Preg is the power request from the driver (that is, the power required for the vehicle to follow the drive cycle). Four positive-power operating modes are defined: motor-only (PSR = 0), engine- only (PSR = 1), power-assist (0 < PSR <1), and recharging (PSR > 1). Figure 16 shows the result of plotting the power split ratio determined by the optimal policy versus the ratio of the requested power and transmission speed. Since the optimal points (dots) group nicely? when plotted against the ratio of
Figure 15: An indirect feedback design and evaluation process using deterministic dynamic optimization As an illustration of how rules are extracted from u*(«,k), define the power split ratio (PSR) as PSR = Peng/Preq, Which can be used to quantify the positive power flows in the powertrain, where P.,,g is the engine power and Preg is the power request from the driver (that is, the power required for the vehicle to follow the drive cycle). Four positive-power operating modes are defined: motor-only (PSR = 0), engine- only (PSR = 1), power-assist (0 < PSR <1), and recharging (PSR > 1). Figure 16 shows the result of plotting the power split ratio determined by the optimal policy versus the ratio of the requested power and transmission speed. Since the optimal points (dots) group nicely? when plotted against the ratio of
Figure 16: An example of extracting the power split ratio (PSR) from the optimal control policy. This is for the UDDSHDV cycle. Similar functions are required for gear selection and regenerative braking. The ‘art’ in the extraction process is determining good regressors. the requested power and transmission speed, regression (solid line) yields a rule for power split that is time invariant, near optimal, and easily implemented on the vehicle. A different choice of drive cycle would yield a different optimal policy, and thus different data (dots in Figure 16) for extracting a rule for power split. However, reference [169] shows that, for the parallel hybrid truck under study, the power split ratio of Figure 16 performs well over several common drive cycles when evaluated on the detailed model.
Figure 16: An example of extracting the power split ratio (PSR) from the optimal control policy. This is for the UDDSHDV cycle. Similar functions are required for gear selection and regenerative braking. The ‘art’ in the extraction process is determining good regressors. the requested power and transmission speed, regression (solid line) yields a rule for power split that is time invariant, near optimal, and easily implemented on the vehicle. A different choice of drive cycle would yield a different optimal policy, and thus different data (dots in Figure 16) for extracting a rule for power split. However, reference [169] shows that, for the parallel hybrid truck under study, the power split ratio of Figure 16 performs well over several common drive cycles when evaluated on the detailed model.
A summary of the direct design method is given in Figure 17. Just as in the deterministic approach, < simplified model is mandatory for computing the optimal policy (again, the curse of dimensionality), anc a detailed model is desirable for evaluating the effectiveness of the strategy. Additional modeling effort is required to represent the planned vehicle use, that is, the drive cycle, as a stationary Markov chain. Ar illustration of the control design process on the parallel hybrid electric truck of Figure 14 is presented ir [170]. An illustration on a hybrid fuel cell vehicle (HFCV) is presented in [144]. As seen in [170] and [144] very ‘realistic’ random driving patterns can result from a Markov power-demand model. The method has not yet been evaluated on hardware. Figure 17: A direct feedback design and evaluation process using stochastic dynamic optimization.
A summary of the direct design method is given in Figure 17. Just as in the deterministic approach, < simplified model is mandatory for computing the optimal policy (again, the curse of dimensionality), anc a detailed model is desirable for evaluating the effectiveness of the strategy. Additional modeling effort is required to represent the planned vehicle use, that is, the drive cycle, as a stationary Markov chain. Ar illustration of the control design process on the parallel hybrid electric truck of Figure 14 is presented ir [170]. An illustration on a hybrid fuel cell vehicle (HFCV) is presented in [144]. As seen in [170] and [144] very ‘realistic’ random driving patterns can result from a Markov power-demand model. The method has not yet been evaluated on hardware. Figure 17: A direct feedback design and evaluation process using stochastic dynamic optimization.
Related topics:
Mechanical EngineeringHybrid VehicleManufacturing EngineeringDiesel engineExhaust Gas RecirculationENGINE CONTROLClean Air ActElectrical and Electronic EngineeringDirect Injection
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