IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v340y2025ics0360544225048194.html

Safety-awareness enhanced eco-driving strategy for dual-motor electric vehicle in highway scenarios based on improved proximal policy optimization algorithm

Author

Listed:
  • Ling, Chenxi
  • Peng, Jiankun
  • Fan, Yi
  • Wang, Zexing
  • Yu, Sichen
  • Wu, Changcheng

Abstract

Ecological driving (eco-driving) is a crucial technique for electric vehicles to reduce energy consumption while maintaining driving quality. This paper proposes an improved PPO-based eco-driving strategy (EDS) for a dual-motor electric vehicle (DMEV) with multiple modes in three-lane highway scenarios. The technique of weighted experience replay (WER) is employed to prioritize data of collision and boundary-violation (CB) to enhance safety awareness, and a mechanism of value function decoupling (VFD) is proposed to improve the accuracy of estimation of state value function. Simulation results demonstrate that the improved PPO method enables the agent to learn a strategy achieving superior performance in driving safety, stability, ride comfort, and energy efficiency, and the performance markedly surpasses those of the baseline methods. The EMS in the proposed model can achieve 98.85 % of energy economy of that of dynamic programming (DP) in the tested traffic flows, and the model has high adaptability to flows with different densities and different levels of battery SoC.

Suggested Citation

  • Ling, Chenxi & Peng, Jiankun & Fan, Yi & Wang, Zexing & Yu, Sichen & Wu, Changcheng, 2025. "Safety-awareness enhanced eco-driving strategy for dual-motor electric vehicle in highway scenarios based on improved proximal policy optimization algorithm," Energy, Elsevier, vol. 340(C).
    • Handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225048194
    DOI: 10.1016/j.energy.2025.139177
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544225048194
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.139177?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Yu, Sichen & Peng, Jiankun & Zhou, Jiaxuan & Ren, Tinghui & Wu, Jingda & Fan, Yi, 2025. "Durability-enhanced decision-making with style awareness for autonomous hydrogen fuel cell vehicle based on integrated reinforcement learning approaches," Energy, Elsevier, vol. 336(C).
    2. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    3. 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.
    4. Ling, Chenxi & Peng, Jiankun & Fan, Yi & Wu, Changcheng & Zhou, Jiaxuan & Yu, Sichen, 2025. "Ecological driving strategy for dual-motor electric vehicles based on sequential hierarchical deep reinforcement learning," Energy, Elsevier, vol. 333(C).
    5. Zheng, Weiguang & Ma, Mengcheng & Xu, Enyong & Huang, Qibai, 2024. "An energy management strategy for fuel-cell hybrid electric vehicles based on model predictive control with a variable time domain," Energy, Elsevier, vol. 312(C).
    6. Fan, Yi & Peng, Jiankun & Yu, Sichen & Yan, Fang & Wang, Zexing & Li, Menglin & Yan, Mei, 2025. "Global optimization guided energy management strategy for hybrid electric vehicles based on generative adversarial network embedded reinforcement learning," Energy, Elsevier, vol. 322(C).
    7. Guan, Changyang & Wang, Junnian & Zheng, Tianhui & Wang, Qikai & Sun, Wen & Wang, Haisheng, 2025. "Parameter optimization and multi-mode operation of a novel dual-motor coupling torque vectoring drive system for electric vehicles," Applied Energy, Elsevier, vol. 389(C).
    8. Zhou, Jiaxuan & Peng, Jiankun & Wu, Jingda & Wei, Zhongbao & Fan, Yi & Guo, Xin, 2024. "Multi-performance enhanced eco-driving strategy for connected fuel cell hybrid electric bus based on stein soft actor-3-critic," Energy, Elsevier, vol. 307(C).
    9. Li, Jie & Liu, Yonggang & Cheng, Jun & Fotouhi, Abbas & Chen, Zheng, 2024. "Eco-driving control for connected plug-in hybrid electric vehicles in urban scenarios with enhanced lane change engagement," Energy, Elsevier, vol. 310(C).
    10. Wu, Changcheng & Peng, Jiankun & Chen, Jun & He, Hongwen & Pi, Dawei & Wang, Zhongwei & Ma, Chunye, 2024. "Battery health-considered energy management strategy for a dual-motor two-speed battery electric vehicle based on a hybrid soft actor-critic algorithm with memory function," Applied Energy, Elsevier, vol. 376(PB).
    11. Liang, Zhaowen & Ruan, Jiageng & Wang, Zhenpo & Liu, Kai & Li, Bin, 2024. "Soft actor-critic-based EMS design for dual motor battery electric bus," Energy, Elsevier, vol. 288(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Wu, Changcheng & Peng, Jiankun & Guo, Xin & Jia, Yongqiang & Cao, Guanghui, 2025. "Evolutionary integrated thermal and energy management strategy based on reinforcement learning for distributed four-wheel drive electric vehicles," Energy, Elsevier, vol. 335(C).
    2. Yu, Sichen & Peng, Jiankun & Zhou, Jiaxuan & Ren, Tinghui & Wu, Jingda & Fan, Yi, 2025. "Durability-enhanced decision-making with style awareness for autonomous hydrogen fuel cell vehicle based on integrated reinforcement learning approaches," Energy, Elsevier, vol. 336(C).
    3. Ling, Chenxi & Peng, Jiankun & Fan, Yi & Wu, Changcheng & Zhou, Jiaxuan & Yu, Sichen, 2025. "Ecological driving strategy for dual-motor electric vehicles based on sequential hierarchical deep reinforcement learning," Energy, Elsevier, vol. 333(C).
    4. Sofiane Bacha & Ramzi Saadi & Mohamed Yacine Ayad & Mohamed Sahraoui & Khaled Laadjal & Antonio J. Marques Cardoso, 2023. "Autonomous Electric-Vehicle Control Using Speed Planning Algorithm and Back-Stepping Approach," Energies, MDPI, vol. 16(5), pages 1-26, March.
    5. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    6. Chen, Jie & Wang, Ruochen & Ding, Renkai & Luo, Ding, 2024. "Matching design and numerical optimization of automotive thermoelectric generator system applied to range-extended electric vehicle," Applied Energy, Elsevier, vol. 370(C).
    7. Feiyu Hou & Fei Yao & Zheng Li, 2022. "A Torque-Compensated Fault-Tolerant Control Method for Electric Vehicle Traction Motor with Short-Circuit Fault," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    8. Lei, Nuo & Zhang, Hao & Hu, Jingjing & Hu, Zunyan & Wang, Zhi, 2025. "Sim-to-real design and development of reinforcement learning-based energy management strategies for fuel cell electric vehicles," Applied Energy, Elsevier, vol. 393(C).
    9. 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).
    10. Jie Hu & Wentong Cao & Feng Jiang & Lingling Hu & Qian Chen & Weiguang Zheng & Junming Zhou, 2023. "Study on Multi-Objective Optimization of Power System Parameters of Battery Electric Vehicles," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    11. Yossi Hadad & Baruch Keren & Dima Alberg, 2023. "An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements," Energies, MDPI, vol. 16(11), pages 1-18, May.
    12. Hicham El Hadraoui & Mourad Zegrari & Fatima-Ezzahra Hammouch & Nasr Guennouni & Oussama Laayati & Ahmed Chebak, 2022. "Design of a Customizable Test Bench of an Electric Vehicle Powertrain for Learning Purposes Using Model-Based System Engineering," Sustainability, MDPI, vol. 14(17), pages 1-22, September.
    13. Pla, Benjamín & Bares, Pau & Aronis, André Nakaema & Anuratha, Sanjith, 2024. "Leveraging battery electric vehicle energy storage potential for home energy saving by model predictive control with backward induction," Applied Energy, Elsevier, vol. 372(C).
    14. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    15. Chen, Mingyi & Yu, Yue & Ouyang, Dongxu & Weng, Jingwen & Zhao, Luyao & Wang, Jian & Chen, Yin, 2024. "Research progress of enhancing battery safety with phase change materials," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PA).
    16. Theo Lieven & Beatrice Hügler, 2021. "Did Electric Vehicle Sales Skyrocket Due to Increased Environmental Awareness While Total Vehicle Sales Declined during COVID-19?," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    17. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).
    18. Mounika, Kandi & Bhattacharjee, Ankur, 2025. "Design and experimental validation for performance analysis of non-isolated power converter topologies in fuel cell integrated dynamic load based local energy systems," Energy, Elsevier, vol. 322(C).
    19. Diana Lemian & Florin Bode, 2022. "Battery-Supercapacitor Energy Storage Systems for Electrical Vehicles: A Review," Energies, MDPI, vol. 15(15), pages 1-13, August.
    20. Yang, Xiong & Peng, Zhenhan & Wang, Pinxi & Zhuge, Chengxiang, 2023. "Seasonal variance in electric vehicle charging demand and its impacts on infrastructure deployment: A big data approach," Energy, Elsevier, vol. 280(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:340:y:2025:i:c:s0360544225048194. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.