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Figure 1. Parameters impacting the energy consumption of battery-electric buses.

Figure 1 Parameters impacting the energy consumption of battery-electric buses.

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Table 1. Parameters affecting the energy consumption of BEB in the literature.
Table 1. Parameters affecting the energy consumption of BEB in the literature.
Table 1. Cont.
Table 1. Cont.
Table 2. A concise list of regression models used to predict energy consumption for BEBs and EV Previous studies concluded that the average speed and acceleration rates signifi- cantly affect energy consumption [45,47]. Intuitively, air conditioning and heating sys- tems increase the consumed energy significantly [45,53]. For topological parameters, the road grade shows the greatest change in energy consumption rates. Moreover, there is a significant range in the consumed energy associated with positive and negative grades [20,45,48,53]. Furthermore, Qi [52] deduced that the consumed energy decreases when the trip distance or ambient temperature increases. Additionally, they argued that energy consumption does not change significantly when the initial state of charge changes. A substantial variation was observed in the energy consumption rates for average nme wel laa bie we OE Less. Lh ceed & Rae A. SRA Aenea CAR AAS A SRA eee, Pha
Table 2. A concise list of regression models used to predict energy consumption for BEBs and EV Previous studies concluded that the average speed and acceleration rates signifi- cantly affect energy consumption [45,47]. Intuitively, air conditioning and heating sys- tems increase the consumed energy significantly [45,53]. For topological parameters, the road grade shows the greatest change in energy consumption rates. Moreover, there is a significant range in the consumed energy associated with positive and negative grades [20,45,48,53]. Furthermore, Qi [52] deduced that the consumed energy decreases when the trip distance or ambient temperature increases. Additionally, they argued that energy consumption does not change significantly when the initial state of charge changes. A substantial variation was observed in the energy consumption rates for average nme wel laa bie we OE Less. Lh ceed & Rae A. SRA Aenea CAR AAS A SRA eee, Pha
Figure 3. MATLAB Simulink model configuration for BEB powertrain. Toward that end, we developed a simulation model to predict BEB’s energy con- sumption using a MATLAB Simulink platform following the approach advised by [2,56], and taking into consideration the block designs used in the advanced vehicle simulator (ADVISOR). Figure 3 depicts the modelling blocks used for model development.
Figure 3. MATLAB Simulink model configuration for BEB powertrain. Toward that end, we developed a simulation model to predict BEB’s energy con- sumption using a MATLAB Simulink platform following the approach advised by [2,56], and taking into consideration the block designs used in the advanced vehicle simulator (ADVISOR). Figure 3 depicts the modelling blocks used for model development.
Figure 4. The longitudinal forces acting on the bus movements. The tractive force (Fr) should be equal or more than the summation of four resistance orces (Equations (2)-(5)) that face the bus during the movement, as illustrated in Figure 4. hese forces include: rolling resistance (Fr), which results from the friction between the tires nd the road (Equation (2)); the magnitude of the rolling force, which is mainly based on the oefficient of rolling friction (C,); vehicle mass (1m); and gravitational acceleration (g) [60]. econd, aerodynamic drag resistance (Frero.) (Equation (3)) is the force required to overcome he air friction. The magnitude of the aerodynamic drag resistance is based on the fronta rea of the bus, air density, aerodynamic drag coefficient, and the speed of the bus [61]. hird, grade resistance (Fg), which depends mainly on the bus mass (1m) and the grade of he roadway (g) (Equation (4)). The force required to accelerate the vehicle (F,) depends on he bus mass and the acceleration/deceleration rates (Equation (5)). If the bus decelerates, he energy resulting from this force is either stored in the battery with regenerative braking nd/or is partially lost in the braking system [35,36,60,61].
Figure 4. The longitudinal forces acting on the bus movements. The tractive force (Fr) should be equal or more than the summation of four resistance orces (Equations (2)-(5)) that face the bus during the movement, as illustrated in Figure 4. hese forces include: rolling resistance (Fr), which results from the friction between the tires nd the road (Equation (2)); the magnitude of the rolling force, which is mainly based on the oefficient of rolling friction (C,); vehicle mass (1m); and gravitational acceleration (g) [60]. econd, aerodynamic drag resistance (Frero.) (Equation (3)) is the force required to overcome he air friction. The magnitude of the aerodynamic drag resistance is based on the fronta rea of the bus, air density, aerodynamic drag coefficient, and the speed of the bus [61]. hird, grade resistance (Fg), which depends mainly on the bus mass (1m) and the grade of he roadway (g) (Equation (4)). The force required to accelerate the vehicle (F,) depends on he bus mass and the acceleration/deceleration rates (Equation (5)). If the bus decelerates, he energy resulting from this force is either stored in the battery with regenerative braking nd/or is partially lost in the braking system [35,36,60,61].
* As Auxiliary power (AUX) is not reported in Altoona Test, a value of Zero was used in the base simulation model. Table 3. Base model parameters for BEB energy consumption simulation. 3.2. Energy Consumption Model Validation = After developing the simulation model, a real-world BEB model is used as our base model. In this respect, we utilized the New Flyer XE40 electric bus, which is a standard 40 ft city transit bus. The input parameters for the base model, including the bus and operation parameters, are listed in Table 3. The charging is provided by a NewFlyer 100 kW portable depot charger. This charger charges four strings of seven lithium-ion batteries that supply power to the Siemens Model 1DB2016 drive motor and bus auxiliaries through the Siemens ELFA2 Electric Drive system.
* As Auxiliary power (AUX) is not reported in Altoona Test, a value of Zero was used in the base simulation model. Table 3. Base model parameters for BEB energy consumption simulation. 3.2. Energy Consumption Model Validation = After developing the simulation model, a real-world BEB model is used as our base model. In this respect, we utilized the New Flyer XE40 electric bus, which is a standard 40 ft city transit bus. The input parameters for the base model, including the bus and operation parameters, are listed in Table 3. The charging is provided by a NewFlyer 100 kW portable depot charger. This charger charges four strings of seven lithium-ion batteries that supply power to the Siemens Model 1DB2016 drive motor and bus auxiliaries through the Siemens ELFA2 Electric Drive system.
Figure 5. Energy consumption in the Altoona test compared to the developed simulation model.
Figure 5. Energy consumption in the Altoona test compared to the developed simulation model.
Table 4. List of input parameters used in full-factorial experiment design.
Table 4. List of input parameters used in full-factorial experiment design.
Figure 6. Full-factorial experimental design parameters (907,199 scenarios).
Figure 6. Full-factorial experimental design parameters (907,199 scenarios).
Table 5. Descriptive statistics of the dataset (n = 907,199). The skewness and kurtosis are computed to test the normality of the data. The skewness values between 0.5 and —0.5 show the normal distribution of the utilized parame- ters [70]. The skewness values closer to zero indicate that the distribution is approximately symmetric, as shown in Table 5. The positive skewness values for V,, P,, Sp, and HVAC indicate that the distribution is highly skewed to the left. Kurtosis between 2.0 and —2.0 is considered acceptable [70]. The descriptive statistics listed in Table 5 show the statistical properties of the pa- rameters utilized in the model. The resultant energy consumption rates ranged between —2.490 kWh/km and 6.119 kWh/km with a mean of 1.654 kWh/km. It should be noted that negative energy consumption values are attributed to scenarios that include a constant negative grade. As such, the energy harnessed from the regenerative brake is higher than the energy consumed by the bus. The average number of passengers is approximately 38 passengers, while the stop density ranged between 1.651 stops/km and 3.486 stops/km. The driver aggressiveness and the road condition have a mean of 2.0 and standard deviation of 0.816, since both are divided into three levels.
Table 5. Descriptive statistics of the dataset (n = 907,199). The skewness and kurtosis are computed to test the normality of the data. The skewness values between 0.5 and —0.5 show the normal distribution of the utilized parame- ters [70]. The skewness values closer to zero indicate that the distribution is approximately symmetric, as shown in Table 5. The positive skewness values for V,, P,, Sp, and HVAC indicate that the distribution is highly skewed to the left. Kurtosis between 2.0 and —2.0 is considered acceptable [70]. The descriptive statistics listed in Table 5 show the statistical properties of the pa- rameters utilized in the model. The resultant energy consumption rates ranged between —2.490 kWh/km and 6.119 kWh/km with a mean of 1.654 kWh/km. It should be noted that negative energy consumption values are attributed to scenarios that include a constant negative grade. As such, the energy harnessed from the regenerative brake is higher than the energy consumed by the bus. The average number of passengers is approximately 38 passengers, while the stop density ranged between 1.651 stops/km and 3.486 stops/km. The driver aggressiveness and the road condition have a mean of 2.0 and standard deviation of 0.816, since both are divided into three levels.
Figure 7. The distribution of the regression standardized residuals.
Figure 7. The distribution of the regression standardized residuals.
* and ** refere to signficane at the 90% and 95% confidence levels, respectvily. Table 6. The correlation matrix for the utilized parameters.
* and ** refere to signficane at the 90% and 95% confidence levels, respectvily. Table 6. The correlation matrix for the utilized parameters.
(h) Average speed vs. road gradiant it full bus occupancy and speed limit of 70 km/h
(h) Average speed vs. road gradiant it full bus occupancy and speed limit of 70 km/h
* Note that: the parameters' terminology had been modified to maintain the terminologies consistency. (For example, the stops per km had been changed to the stop density).
* Note that: the parameters' terminology had been modified to maintain the terminologies consistency. (For example, the stops per km had been changed to the stop density).
Table 8. Energy consumption scenarios for BEB route selection.
Table 8. Energy consumption scenarios for BEB route selection.
Figure A1. Altoona’s combined speed profiles.
Figure A1. Altoona’s combined speed profiles.
Appendix B Autonomie-Based Validation of the Developed Simulink Model Figure A2. Autonomie based energy consumption model validation.
Appendix B Autonomie-Based Validation of the Developed Simulink Model Figure A2. Autonomie based energy consumption model validation.
Figure A3. A scatter plot for the regression standardized residuals (n = 907,199)
Figure A3. A scatter plot for the regression standardized residuals (n = 907,199)
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