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Design of a Rule-Based Controller and Parameter Optimization Using a Genetic Algorithm for a Dual-Motor Heavy-Duty Battery Electric Vehicle
ISSN: 0148-7191, e-ISSN: 2688-3627
Published March 29, 2022 by SAE International in United States
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This paper describes a configuration and controller, designed using Autonomie,1 for dual-motor battery electric vehicle (BEV) heavy-duty trucks. Based on the literature and current market research, this model was designed with two electric motors, one on the front axle and the other on the rear axle. A rule-based control algorithm was designed for the new dual-motor BEV, based on the model, and the control parameters were optimized by using a genetic algorithm (GA). The model was simulated in diverse driving cycles and gradeability tests. The results show both a good following of the desired cycle and achievement of truck gradeability performance requirements. The simulation results were compared with those of a single-motor BEV and showed reduced energy consumption with the high-efficiency operation of the two motors.
CitationYu, K., Vijayagopal, R., and Kim, N., "Design of a Rule-Based Controller and Parameter Optimization Using a Genetic Algorithm for a Dual-Motor Heavy-Duty Battery Electric Vehicle," SAE Technical Paper 2022-01-0413, 2022, https://doi.org/10.4271/2022-01-0413.
- Chopra , S. and Bauer , P. Driving Range Extension of EV with On-Road Contactless Power Transfer—A Case Study IEEE Transactions on Industrial Electronics 60 1 2013 329 338 https://doi.org/10.1109/TIE.2011.2182015
- Davis , S.C. and Boundy , R.G. 2021 https://doi.org/10.2172/1767864
- Gao , Y. , Wang , W. , and Li , Y. Optimization of Control Strategy for Dual-Motor Coupling Propulsion System Based on Dynamic Programming Method 3rd Conference on Vehicle Control and Intelligence (CVCI) 2019 1 6 https://doi.org/10.1109/CVCI47823.2019.8951627
- Hu , J. , Niu , X. , Jiang , X. , and Zu , G. Energy Management Strategy Based on Driving Pattern Recognition for a Dual-Motor Battery Electric Vehicle International Journal of Energy Research 43 8 2019 3346 3364 https://doi.org/10.1002/er.4474
- Nieto Prada , D. , Vijayagopal , R. , and Costanzo , V. Opportunities for Medium and Heavy Duty Vehicle Fuel Economy Improvements through Hybridization SAE Technical Paper 2021-01-0717 2021 https://doi.org/10.4271/2021-01-0717
- Ehsan Sabri , I. , Vijayagopal , R. , Moawad , A. , Kim , N. et al. 2021 https://vms.es.anl.gov/case-studies/u-s-doe-vto-hfto-r-d-benefits/
- Ruan , J. and Song , Q. A Novel Dual-Motor Two-Speed Direct Drive Battery Electric Vehicle Drivetrain IEEE Access 7 2019 54330 54342 https://doi.org/10.1109/ACCESS.2019.2912994
- Tesla 2021 https://www.tesla.com/semi
- Volvo 2021 https://www.volvotrucks.com/en-en/trucks/alternative-fuels/electric-trucks.html
- Wu , B. and Zhang , S. Energy Management Strategy for Dual-Motor Two-Speed Transmission Electric Vehicles Based on Dynamic Programming Algorithm Optimization SAE Int. J. Elec. Veh. 10 1 2020 19 31 https://doi.org/10.4271/14-10-01-0002
- Zhang , C. , Zhang , S. , Han , G. , and Liu , H. Power Management Comparison for a Dual-Motor-Propulsion System Used in a Battery Electric Bus IEEE Transactions on Industrial Electronics 64 2017 3873 3882 https://doi.org/10.1109/TIE.2016.2645166
- Zhao , M. , Shi , J. , and Lin , C. Energy Management Strategy Design for Dual-Motor Coaxial Coupling Propulsion Electric City-Buses Energy Procedia 152 2018 568 573 https://doi.org/10.1016/j.egypro.2018.09.212
- Zhao , M. , Zhang , R. , Lin , C. , Zhou , H. et al. Stochastic Model Predictive Control for Dual-Motor Battery Electric Bus Based on Signed Markov Chain Monte Carlo Method IEEE Access 8 2020 120785 120797 https://doi.org/10.1109/ACCESS.2020.3006285
- Zheng , Q. , Tian , S. , Zhang , Q. , and Chen , C.H. Optimal Torque Split Strategy of Dual-Motor Electric Vehicle Using Adaptive Nonlinear Particle Swarm Optimization Mathematical Problems in Engineering 2020 2020 https://doi.org/10.1155/2020/1204260