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Powertrain Modeling and Range Estimation of the Electric Bus

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Powertrain Modeling and Range Estimation of the Electric Bus Powertrain Modeling and Range Estimation of The Electric Bus Donkyu Baek* [donkyu at cbnu.ac.kr] School of Electronics Engineering Chungbuk National University Presenter Dr. Donkyu Baek Assistant Professor in Chungbuk National University, Korea Got Ph.D. in KAIST (Korea Advanced Institute of Science and Technology), Korea Post doc experiences Mar. 2018 ~ Dec. 2018: KAIST, Korea Jan. 2019 ~ Jan. 2020: Politecnico di Torino, Italy Research interest EV/drone powertrain modeling EV/drone runtime optimization EV/drone design-time optimization 2 EVs Are Invading the ICEV Market Higher efficiency Average 141 kWh for ICEVs and 20 kWh for EVs when drive 100 km Environmental friendliness + government promotion Quiet and comfort ride Electric vehicles Gasoline vehicles 400400 Veyron 300300 www.fueleconomy.gov Escalade GL63 AMG Mustang M5 200200 Corvette (kWh/100 mi) (kWh/100 100 100 Prius (hybrid) Energy consumption consumption Energy Prius (electric) Volt Twizy Leaf Model S 0 1625 3250 4875 6500 0 1625 3250 4875 6500 Curb weights (lbs) 3 Limitation of EVs EVs still cannot reach the territory of ICEVs Short driving range 27 kWh battery pack (Soul EV) versus 514 kWh fuel tank (Soul) Long charging time 5 hours with level 2 and 30 minutes with level 3 battery charging systems Driving ranges from Los Angeles Tesla Model S (350 km) Toyota RAV4 (166 km) tia.ucsb.edu 4 3 Table 3 Battery Motor power Gear ratio Energy Battery size Energy Energy Driving Driving Note 220 Issue Target driving profile pack (kW) (times) consumption (kJ) (kWh) consumption (kWh) consumption (%) Drivetraincycle range (%) - Velocity/acceleration Optimal 700 10 1 2479 190 17.64 0.688 100%components26 library100.0% over time - Road slope by distance ×0.5 battery 300 10 1 2256 7.56 0.627 91% 12 47.1% size 160 500 10 1 2367 (ft) Elevation 12.6 0.657 95% 19 74.8% Vehicle performance 130 - Maximum velocity/ 600 10 1 2424 0.015.12 0.9 1.80.673 2.8 98% 22 87.6% Energy-Efficient EV Vehicle simulator acceleration 1000 10 1 2618 30 25.2 0.727 106%Electric 35vehicle135.3% Energy consumption ×2 battery 1400 10 1 277322.5 35.28 0.770 112%new candidate46 178.8% - Battery SoC size generation 15 Energy consumption is reduced by 50% ×with3 battery optimal2100 driving10 and1 by 2909 52.92 0.808 117%- Motor size65 255.6% size - Battery capacity 20% with optimal vehicle design 7.5 Pareto set update ×0.5 motor 700 5 1 2429 (mph) Velocity 17.64 0.675 98% 26 102.1% size with 2 0 gear ratio Start/end 0.0 0.9 1.8 2.8 ×0.8 motor 700 8 Mail1 drop 2372 17.64 0.659 96% 27 104.5% No End of size Distance (mile) Genetic algorithm Then, how to find the optimal vehicle driving and design? generation? ×2 motor 700 (a) 20 1 2564 17.64 (b) 0.712 103% 25 96.7% size Yes ×3 motor 700 30 1 2617 17.64 0.727 106% 24 94.7% sizeFig. 2. (a) A campus mail delivery route and (b) road elevation change and Solution set 0 0.000 0% driving cycle. - Vehicle performance 0 0.000 0% - Driving range - MSRP Energy consumption (%) Driving range (%) Iterative search loop 120% 300% 110 110% Fig. 4. Existing electric vehicle design-time optimization dedicated to a 100 200% driving mission. 100% 90 100% 90% 80 Build Your Own Electric Vehicle Battery energy (Wh) Irvine, CA 92602 70 80% 0% Home 10 Work 900 University Ave, Riverside, CA 92521 0.25 5 0.2 Primary purpose Work Leisure Optimal Optimal 0.15 Velocity (m/s) To work 7:30 AM From work 5:50 PM 0 0.1 size 2 motor 3 motor size 2 motor size 3 motor size 2 size 2 battery size 3 battery size 2 battery size 3 battery × × × × Acceleration (m/s ) × × × × 0.5 battery size 0.5 battery size 0.5 battery Driving range 3 driving cycles × × 5 Reset Submit Fig. 3. Example electric vehicle setups and energy consumption comparison. (a) Build Your Own Electric Vehicle Motor power: 150 kW should be recharged during mail delivery, and it also makes Battery: 60 kWh battery depreciation faster due to a more charging cycles. 0-60 mph: 7.3 seconds Top speed: 90 mph Such design-time optimization shows lots of opportunities for Range: 3 commutes per charging application-specific electric vehicle optimization. MSRP: $39,000 Performance Performance Now the question is how to find the optimal setup in Back downgrade upgrade a more systematic way. Previous work finds the minimum- energy electric vehicle specifications from a set of batteries (b) and motors for a given route profile using Genetic algorithm. This is a multi-variable optimization problem that evaluates Fig. 5. An example of graphical user interface of the rapid energy-aware electric vehicle synthesis. the total energy consumption for the given route. The Genetic algorithm generates a large number of powertrain specification setups [11]. We illustrate the existing framework to find the features as well as the types of the engine via a web interface. optimal electric vehicle configuration in Fig. 4. This method However, they can just confirm the MSRP (mean suggested derives the specification of an electric vehicle that results in retail price) after they build their vehicle. The same is true for the minimum energy consumption for a given route while computer purchase. Customers can only compare the price, satisfying the vehicle performance such as the driving range but non-tech-savvy people do not have a concrete idea what as well as the maximum acceleration and velocity. However, the right configuration is for their purpose. this method should run a vehicle simulator in the inner-loop A rapid energy-aware electric vehicle synthesis helps cus- for each iterative search. This is because!1 the traction force, tomers to find a right electric vehicle configuration for their curb weight, power consumption and driving range are all own purpose. The synthesis time should be shorter than a dependent among each other. second not to challenge the user’s patience as well as not to cause misperception of service interruption. Unfortunately, the V. A RAPID SYNTHESIS OF ENERGY-EFFICIENT ELECTRIC existing method cannot afford instant evaluations, which are VEHICLES commonly required in Web environments. Nowadays, customers can build a vehicle by their preference Fig. 5 shows a new electric vehicle selection function for such as the color, exterior, interior, entertainment, safety non-tech-savvy customers. Customers are supposed to pick an System-Level Approach for EV Optimization Low-power electric vehicle system-level approach Optimal driving profile EV driving optimization Road/traffic info. Vehicle setup EV characterization Driving constraints Power consumption model Energy harvesting/ EV power consumption simulator charging model Battery model EV design optimization Road/traffic info. Driving profile Component library (cost, life, etc.) Optimal vehicle 6 design candidates IT Technology Inspired EV Design Framework Electric vehicle power consumption simulation Vehicle input parameters Battery Motor characteristics Mechanical specifications Transportation input parameters EV power Battery Passenger information consumption SoC simulator Driving input parameters Driving route Speed and acceleration 7 IT Technology Inspired EV Design Framework ADvanced VehIcle SimulatOR (ADVISOR) works well to estimate 1) vehicle powertrain and 2) battery SoC Vehicle input parameters Battery Motor characteristics Mechanical specifications Transportation input parameters EV power Battery Passenger information consumption SoC simulator Driving input parameters Driving route Speed and acceleration 8 IT Technology Inspired EV Design Framework ADvanced VehIcle SimulatOR (ADVISOR) Vehicle type Input parameters Powertrain operation range and efficiency map 9 EV Power Consumption Characterization ADVISOR is a fast simulator BUT not proper to run in the inner loop of the optimization algorithms Vehicle input parameters Battery Not acceptable for online loop-based Motor characteristics optimization methods Mechanical specifications Transportation input parameters EV power Battery Passenger information consumption SoC simulator Driving input parameters Driving route Speed and acceleration 10 EV Power Consumption Characterization Need to implement very simple but accurate power model using ADVISOR Extract input parameters of powertrain Driving simulation 100 Powertrain spec. 50 4 (km/h) Speed 0 0 44 88 132 176 220 264 308 352 396 440 484 528 572 616 660 704 748 ADVISOR simulation result Model coefficient extraction result 100 Time (s) 30 50 0 (kW) Power 0 -30 0 Power (kW) 44 88 -50 132 176 220 264 308 352 396 440 484 528 572 616 660 704 748 0 1000 2000 3000 4000 5000 6000 Time (s) Time (s) Fig. 1: Validation of the model coefficient extraction. TABLE I: Model coefficients for Chevrolet Bolt. Logging Summary GM Bolt (use accumulated power) a 0.06 b 9.5549 g 1.0013 25 mile driving 65 mile driving 75 mile driving 93 mile driving Constant driving 25 65 75 93 d 0.00012 C0 1000 C1 10.588 (mile) Time ess_pwr_r Energy ess_pwr_r Energy ess_pwr_ Energy ess_pwr_r Energy Sum (25 mile) 1325930.16 C2 8.11 C3 0.00032 e 0.6633 (s) (W) (Ws) (W) (Ws) r (Ws) (W) (Ws) Constant driving (km/ 40 105 121 150 z 5813.6 (W) Energy total (kWh) 0.37 h) 0 3678.94 3678.94 15706.27 15706.27 21262.37 21262.37 34795.05 34795.05 Used (%) 0.64% Driving distance (km) 4.02 10.46 12.1 14.5 20 1 3683.15 3683.15 15749.83 15749.83 21337.85 21337.85 34952.66 34952.66 SoC (%) 99.36% 97.26% 96.28% 93.91% 50 -6 40 Electric-4 vehicle model-0.3 Equation form 2 3683.15 3683.15 15749.83 15749.83 21337.85 21337.85 34952.66 34952.66 Sum (65 mile) 5669894.31 Range (km) 626.97 381.22 325.50 238.13 0 30 -2 16 3 3683.15 3683.15 15749.83 15749.83 21337.85 21337.85 34952.66 34952.66 Energy total (kWh) 1.57 Range (mile) 0.4 20 in0 degree vehicle simulator EV power model 4 3683.15 3683.15 15749.83 15749.83 21337.85 21337.85 34952.66 34952.66 Used (%) 2.74% CD = 0.200 % 0.67% -5.17% 6.45% -7.52% 12 Driving data 2 1.0 degree 5 3683.15 3683.15 15749.83 15749.83 21337.85 21337.85 34952.66 34952.66 Reference range (km) 622.816 402 305.775 257.5 10 4 8 6 3683.15 3683.15 15749.83 15749.83 21337.85 21337.85 34952.66 34952.66 Sum (75 mile) 7681551.19 6 1.7 2 2 7 3683.15 3683.15 15749.83 15749.83 21337.85 21337.85 34952.66 34952.66 Energy total (kWh) 2.13 (a) Range-speed experimental data.
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