IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v324y2025ics036054422501477x.html

Co-optimization of speed planning and energy management for connected plug-in hybrid electric vehicles on slippery roads

Author

Listed:
  • Ma, Zheng
  • Zhang, Fengqi
  • Xie, Shaobo
  • Zhang, Tao
  • Chen, Yingying
  • Peng, Guozhi

Abstract

With the development of Vehicle-to-Everything (V2X) technology, optimizing speed planning and energy management for Connected Plug-in Hybrid Electric Vehicles (CPHEVs) under complex conditions has become crucial. However, existing studies primarily focus on car-following scenarios with a single leading vehicle under ideal road conditions, neglecting the increase in vehicle stability and control risks on slippery roads. To address this gap, this study proposes an Improved Variable Time Headway (IVTH) strategy, integrated into a Model Predictive Control (MPC)-based optimization framework (IVTH-MPC) for multi-lead vehicle following scenarios on slippery roads. The proposed strategy enhances traditional VTH by incorporating relative speed, position, and acceleration differences among multi-lead vehicles, improving dynamic perception and prediction capabilities. Additionally, the IVTH-MPC strategy integrates adhesion coefficient constraints into the MPC optimization, achieving coordinated optimization of safety, comfort, and economy. The results show that compared to the traditional VTH-MPC, the IVTH-MPC reduces the maximum, minimum, and average absolute acceleration by 26.67 %, 6.67 %, and 16.90 %, respectively, thereby lowering the risk of instability and improving ride comfort. Moreover, the IVTH-MPC can respond (in braking or accelerating) 1–2 s in advance of the dynamic changes of the leading vehicles, while improving fuel economy with a 7.91 % reduction in total cost. Furthermore, adaptability is evaluated to verify the effectiveness of IVTH-MPC under different driving conditions.

Suggested Citation

  • Ma, Zheng & Zhang, Fengqi & Xie, Shaobo & Zhang, Tao & Chen, Yingying & Peng, Guozhi, 2025. "Co-optimization of speed planning and energy management for connected plug-in hybrid electric vehicles on slippery roads," Energy, Elsevier, vol. 324(C).
    • Handle: RePEc:eee:energy:v:324:y:2025:i:c:s036054422501477x
    DOI: 10.1016/j.energy.2025.135835
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422501477X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2025.135835?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. 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).
    2. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    3. Yu, Shaowei & Huang, Mengxing & Ren, Jia & Shi, Zhongke, 2016. "An improved car-following model considering velocity fluctuation of the immediately ahead car," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 449(C), pages 1-17.
    4. Gao, Kai & Luo, Pan & Xie, Jin & Chen, Bin & Wu, Yue & Du, Ronghua, 2023. "Energy management of plug-in hybrid electric vehicles based on speed prediction fused driving intention and LIDAR," Energy, Elsevier, vol. 284(C).
    5. Xue, Jiaqi & Jiao, Xiaohong & Yu, Danmei & Zhang, Yahui, 2023. "Predictive hierarchical eco-driving control involving speed planning and energy management for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 283(C).
    6. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    7. Ramin Ahadi & Wolfgang Ketter & John Collins & Nicolò Daina, 2023. "Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets," Transportation Science, INFORMS, vol. 57(3), pages 613-630, May.
    8. Sun, Chao & Zhang, Chuntao & Sun, Fengchun & Zhou, Xingyu, 2022. "Stochastic co-optimization of speed planning and powertrain control with dynamic probabilistic constraints for safe and ecological driving," Applied Energy, Elsevier, vol. 325(C).
    9. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    10. Li, Cheng & Xu, Xiangyang & Zhu, Helong & Gan, Jiongpeng & Chen, Zhige & Tang, Xiaolin, 2024. "Research on car-following control and energy management strategy of hybrid electric vehicles in connected scene," Energy, Elsevier, vol. 293(C).
    11. Zhou, Wei & Cai, Xuan & Chen, Yaoqi & Li, Junqiu & Peng, Xiaoyan, 2022. "Decoding the optimal charge depletion behavior in energy domain for predictive energy management of series plug-in hybrid electric vehicle," Applied Energy, Elsevier, vol. 316(C).
    12. Zhou, Wei & Chen, Yaoqi & Zhai, Haoran & Zhang, Weigang, 2021. "Predictive energy management for a plug-in hybrid electric vehicle using driving profile segmentation and energy-based analytical SoC planning," Energy, Elsevier, vol. 220(C).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhou, Quan & Du, Changqing & Yan, Yunbing & Chen, Zhengfu, 2025. "A tolerant sequential predictive energy management strategy for the platoon hybrid electric vehicle with the distributed driving optimization," Energy, Elsevier, vol. 335(C).

    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. Nie, Zhigen & Feng, Yaxing & Lian, Yufeng, 2025. "Deep reinforcement learning-based hierarchical control strategy for energy management of intelligent fuel cell hybrid electric vehicles," Energy, Elsevier, vol. 326(C).
    2. Huang, Ruchen & He, Hongwen & Su, Qicong, 2024. "An intelligent full-knowledge transferable collaborative eco-driving framework based on improved soft actor-critic algorithm," Applied Energy, Elsevier, vol. 375(C).
    3. Wang, Shuhan & Yao, Kun & Guo, Wei & Xu, Xiangyang & Liu, Yiqiang & Qian, Pengfei & Zhao, Junwei & Dong, Peng, 2025. "Approximate optimal energy management strategy for multi-speed series-parallel PHEV integrating global prediction and real-time control," Energy, Elsevier, vol. 335(C).
    4. Zhang, Hao & Lei, Nuo & Chen, Boli & Li, Bingbing & Li, Rulong & Wang, Zhi, 2024. "Modeling and control system optimization for electrified vehicles: A data-driven approach," Energy, Elsevier, vol. 310(C).
    5. Cai, Xuan & Zhou, Wei & Cui, Zhiyong & Bai, Xuesong & Liu, Fan & Yu, Haiyang & Ren, Yilong, 2024. "An explicit State-of-Charge planning solution for plug-in hybrid electric vehicle based on low-granularity prior-knowledge," Energy, Elsevier, vol. 313(C).
    6. Tang, Wenbin & Wang, Yaqian & Jiao, Xiaohong & Ren, Lina, 2023. "Hierarchical energy management strategy based on adaptive dynamic programming for hybrid electric vehicles in car-following scenarios," Energy, Elsevier, vol. 265(C).
    7. Yong Wang & Jingda Wu & Hongwen He & Zhongbao Wei & Fengchun Sun, 2025. "Data-driven energy management for electric vehicles using offline reinforcement learning," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
    8. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    9. Chen, Bin & Wang, Miaoben & Hu, Lin & He, Guo & Yan, Haoyang & Wen, Xinji & Du, Ronghua, 2024. "Data-driven Koopman model predictive control for hybrid energy storage system of electric vehicles under vehicle-following scenarios," Applied Energy, Elsevier, vol. 365(C).
    10. Li, Daofei & Jiang, Yangye & Shen, Yijie, 2024. "Intersection eco-driving for automated vehicles: SMPC-based strategies for handling leading vehicle starting-up uncertainties," Energy, Elsevier, vol. 302(C).
    11. Hu, Dong & Xie, Hui & Song, Kang & Zhang, Yuanyuan & Yan, Long, 2023. "An apprenticeship-reinforcement learning scheme based on expert demonstrations for energy management strategy of hybrid electric vehicles," Applied Energy, Elsevier, vol. 342(C).
    12. Wang, Zhong & Zhao, Yahui & Zhang, Yahui & Tian, Yang & Jiao, Xiaohong, 2024. "Safe off-policy reinforcement learning on eco-driving for a P2-P3 hybrid electric truck," Energy, Elsevier, vol. 313(C).
    13. Li, Jianwei & Liu, Jie & Yang, Qingqing & Wang, Tianci & He, Hongwen & Wang, Hanxiao & Sun, Fengchun, 2025. "Reinforcement learning based energy management for fuel cell hybrid electric vehicles: A comprehensive review on decision process reformulation and strategy implementation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 213(C).
    14. Li, Yuxi & Zhang, Yu & Hao, Gang, 2025. "Stochastic energy model predictive control for cooperative adaptive cruising," Energy, Elsevier, vol. 334(C).
    15. Hu, Dong & Huang, Chao & Wu, Jingda & Wei, Henglai & Pi, Dawei, 2025. "Enhancing data-driven energy management strategy via digital expert guidance for electrified vehicles," Applied Energy, Elsevier, vol. 381(C).
    16. Cui, Wei & Cui, Naxin & Li, Tao & Du, Yi & Zhang, Chenghui, 2024. "Multi-objective hierarchical energy management for connected plug-in hybrid electric vehicle with cyber–physical interaction," Applied Energy, Elsevier, vol. 360(C).
    17. Zhou, Yang & Chen, Bo & Sun, Sha & Ma, Ruiqing & Xie, Yuchen & Wu, Yue & Jiang, Wentao & Bai, Hao & Ma, Rui, 2025. "Towards cost mitigation for fuel cell electric vehicles: Recent progress and challenges on combined optimization of powertrain sizing configuration, speed planning and energy management strategies," Applied Energy, Elsevier, vol. 402(PA).
    18. Sumitkumar, Rathor & Al-Sumaiti, Ameena Saad, 2024. "Shared autonomous electric vehicle: Towards social economy of energy and mobility from power-transportation nexus perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 197(C).
    19. Matteo Acquarone & Claudio Maino & Daniela Misul & Ezio Spessa & Antonio Mastropietro & Luca Sorrentino & Enrico Busto, 2023. "Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control," Energies, MDPI, vol. 16(6), pages 1-22, March.
    20. Xie, Shaobo & Qi, Shanwei & Lang, Kun & Tang, Xiaolin & Lin, Xianke, 2020. "Coordinated management of connected plug-in hybrid electric buses for energy saving, inter-vehicle safety, and battery health," Applied Energy, Elsevier, vol. 268(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:324:y:2025:i:c:s036054422501477x. 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.