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A review of the design process of energy management systems for dual-motor battery electric vehicles

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  • Louback, Eduardo
  • Biswas, Atriya
  • Machado, Fabricio
  • Emadi, Ali

Abstract

Dual-motor battery electric vehicles (DM-BEVs) are a trending technology in the electric vehicle market. They have the potential to achieve higher energy savings and dynamic performances compared to single-speed, single-motor BEVs. However, a more complex and robust energy management system (EMS) is needed to achieve these benefits. Hence, this work reviews the design process and real-time implementation of EMSs tailored for DM-BEVs, starting from the fundamental concepts of two-motor coupling. The advantages and disadvantages of the most popular dual-motor architectures and their influence on the EMS design complexity are presented, followed by a revision of the reported energy management controllers. Besides the most prominent methods, classified as rule-based or optimization-based techniques, reinforcement learning-based EMSs are discussed in detail, given their near-optimal, real-time implementation and adaptability to newer, unforeseen drive cycles. Finally, the standard procedures and equipment required to assess the EMS’ performance with hardware-in-the-loop tests are presented. Conclusions and open challenges for the energy management controllers of DM-BEVs are discussed at the end of this work.

Suggested Citation

  • Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
  • Handle: RePEc:eee:rensus:v:193:y:2024:i:c:s1364032124000169
    DOI: 10.1016/j.rser.2024.114293
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    as
    1. Ruan, Jiageng & Walker, Paul D. & Watterson, Peter A. & Zhang, Nong, 2016. "The dynamic performance and economic benefit of a blended braking system in a multi-speed battery electric vehicle," Applied Energy, Elsevier, vol. 183(C), pages 1240-1258.
    2. Hong, Xianqian & Wu, Jinglai & Zhang, Nong & Wang, Bing, 2022. "Energy efficiency optimization of Simpson planetary gearset based dual-motor powertrains for electric vehicles," Energy, Elsevier, vol. 259(C).
    3. Zhang, Shuo & Xiong, Rui & Zhang, Chengning, 2015. "Pontryagin’s Minimum Principle-based power management of a dual-motor-driven electric bus," Applied Energy, Elsevier, vol. 159(C), pages 370-380.
    4. Mingjie Zhao & Junhui Shi & Cheng Lin & Junzhi Zhang, 2018. "Application-Oriented Optimal Shift Schedule Extraction for a Dual-Motor Electric Bus with Automated Manual Transmission," Energies, MDPI, vol. 11(2), pages 1-16, February.
    5. Kwon, Kihan & Seo, Minsik & Min, Seungjae, 2020. "Efficient multi-objective optimization of gear ratios and motor torque distribution for electric vehicles with two-motor and two-speed powertrain system," Applied Energy, Elsevier, vol. 259(C).
    6. Pedro Daniel Urbina Coronado & Horacio Ahuett-Garza, 2015. "Control Strategy for Power Distribution in Dual Motor Propulsion System for Electric Vehicles," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, November.
    7. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(C).
    8. Zou, Runnan & Fan, Likang & Dong, Yanrui & Zheng, Siyu & Hu, Chenxing, 2021. "DQL energy management: An online-updated algorithm and its application in fix-line hybrid electric vehicle," Energy, Elsevier, vol. 225(C).
    9. Wei, Hongqian & Zhang, Nan & Liang, Jun & Ai, Qiang & Zhao, Wenqiang & Huang, Tianyi & Zhang, Youtong, 2022. "Deep reinforcement learning based direct torque control strategy for distributed drive electric vehicles considering active safety and energy saving performance," Energy, Elsevier, vol. 238(PB).
    10. Zhou, Quan & Li, Ji & Shuai, Bin & Williams, Huw & He, Yinglong & Li, Ziyang & Xu, Hongming & Yan, Fuwu, 2019. "Multi-step reinforcement learning for model-free predictive energy management of an electrified off-highway vehicle," Applied Energy, Elsevier, vol. 255(C).
    11. Lin, Xinyou & Li, Yalong & Zhang, Guangji, 2022. "Bi-objective optimization strategy of energy consumption and shift shock based driving cycle-aware bias coefficients for a novel dual-motor electric vehicle," Energy, Elsevier, vol. 249(C).
    12. Zhao, Mingjie & Shi, Junhui & Lin, Cheng, 2019. "Optimization of integrated energy management for a dual-motor coaxial coupling propulsion electric city bus," Applied Energy, Elsevier, vol. 243(C), pages 21-34.
    13. Qingxing Zheng & Shaopeng Tian & Qian Zhang, 2020. "Optimal Torque Split Strategy of Dual-Motor Electric Vehicle Using Adaptive Nonlinear Particle Swarm Optimization," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-21, May.
    14. Kritika Deepak & Mohamed Amine Frikha & Yassine Benômar & Mohamed El Baghdadi & Omar Hegazy, 2023. "In-Wheel Motor Drive Systems for Electric Vehicles: State of the Art, Challenges, and Future Trends," Energies, MDPI, vol. 16(7), pages 1-31, March.
    15. Yang, Ningkang & Han, Lijin & Xiang, Changle & Liu, Hui & Li, Xunmin, 2021. "An indirect reinforcement learning based real-time energy management strategy via high-order Markov Chain model for a hybrid electric vehicle," Energy, Elsevier, vol. 236(C).
    16. Jiajia Liang & Xiangyang Xu & Peng Dong & Tao Feng & Wei Guo & Shuhan Wang, 2022. "Energy Management Strategy of a Novel Electric Dual-Motor Transmission for Heavy Commercial Vehicles Based on APSO Algorithm," Sustainability, MDPI, vol. 14(3), pages 1-12, January.
    17. Yong Wang & Dongye Sun, 2014. "Powertrain Matching and Optimization of Dual-Motor Hybrid Driving System for Electric Vehicle Based on Quantum Genetic Intelligent Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2014, pages 1-11, November.
    18. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    19. Saidur, R., 2010. "A review on electrical motors energy use and energy savings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 877-898, April.
    20. Chen, Zheng & Wu, Simin & Shen, Shiquan & Liu, Yonggang & Guo, Fengxiang & Zhang, Yuanjian, 2023. "Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios," Energy, Elsevier, vol. 263(PF).
    21. Wang, Zhenzhen & Zhou, Jun & Rizzoni, Giorgio, 2022. "A review of architectures and control strategies of dual-motor coupling powertrain systems for battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    22. Zhenzhen Lei & Dong Cheng & Yonggang Liu & Datong Qin & Yi Zhang & Qingbo Xie, 2017. "A Dynamic Control Strategy for Hybrid Electric Vehicles Based on Parameter Optimization for Multiple Driving Cycles and Driving Pattern Recognition," Energies, MDPI, vol. 10(1), pages 1-20, January.
    23. Du, Guodong & Zou, Yuan & Zhang, Xudong & Kong, Zehui & Wu, Jinlong & He, Dingbo, 2019. "Intelligent energy management for hybrid electric tracked vehicles using online reinforcement learning," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    24. Jianjun Hu & Lingling Zheng & Meixia Jia & Yi Zhang & Tao Pang, 2018. "Optimization and Model Validation of Operation Control Strategies for a Novel Dual-Motor Coupling-Propulsion Pure Electric Vehicle," Energies, MDPI, vol. 11(4), pages 1-14, March.
    25. Li Zhai & Rufei Hou & Tianmin Sun & Steven Kavuma, 2018. "Continuous Steering Stability Control Based on an Energy-Saving Torque Distribution Algorithm for a Four in-Wheel-Motor Independent-Drive Electric Vehicle," Energies, MDPI, vol. 11(2), pages 1-19, February.
    26. Piotr Bielaczyc & Rafal Sala & Tomasz Meinicke, 2021. "Analysis of Technical Capabilities, Methodology and Test Results of a Light-Commercial Vehicle Conversion to Battery Electric Powertrain," Energies, MDPI, vol. 14(4), pages 1-18, February.
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