IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i7p3232-d1628367.html

Research on Online Energy Management Strategy for Hybrid Energy Storage Electric Vehicles Under Adaptive Cruising Conditions

Author

Listed:
  • Zhiwen Zhang

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China
    Hebei Key Laboratory of Specialized Transportation Equipment, Qinhuangdao 066004, China)

  • Jie Tang

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China)

  • Jiyuan Zhang

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China)

  • Tianyu Li

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China)

  • Hao Chen

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066000, China
    Hebei Key Laboratory of Specialized Transportation Equipment, Qinhuangdao 066004, China)

Abstract

To address the critical challenge of high energy consumption in single-source electric vehicles, this study proposes a hybrid energy storage system (HESS)-integrated energy management strategy (EMS). Firstly, the car-following and HESS models are constructed. Secondly, a multi-objective optimization framework balancing adaptive cruise control (ACC) optimal tracking quality and energy economy is developed, where the fast, non-dominated sorting genetic algorithm (NSGA-II) resolves dynamic power demands. Thirdly, the third-order Haar wavelet enables online rolling decomposition of power profiles. The high-frequency transient power is matched by a supercapacitor, while the low-frequency steady-state power is utilized as an input variable to the optimization controller. Then, a fuzzy logic controller dynamically optimizes HESS’s energy distribution based on state-of-charge (SOC) and load conditions. Finally, the cruise simulation model has been constructed utilizing the MATLAB/Simulink platform. Comparative analysis under the Urban Dynamometer Driving Schedule (UDDS) demonstrates a 3.71% reduction in the total power demand of the ego vehicle compared to the front vehicle. Compared to single-source configurations, the HESS ensures smoother SOC dynamics in lithium-ion batteries. After employing the third-order Haar wavelet for online rolling decomposition of the demand power, the high-frequency transient power matched by the lithium-ion battery is substantially reduced. Comparative analysis of three control strategies demonstrates that the wavelet-fuzzy logic approach exhibits superior comprehensive performance. Consequently, the proposed strategy effectively mitigates high-frequency transient peak charge/discharge currents in the lithium-ion battery and the energy consumption of the entire vehicle. This study provides a novel solution for energy storage systems in hybrid energy storage electric vehicles (HESEV) under ACC scenarios.

Suggested Citation

  • Zhiwen Zhang & Jie Tang & Jiyuan Zhang & Tianyu Li & Hao Chen, 2025. "Research on Online Energy Management Strategy for Hybrid Energy Storage Electric Vehicles Under Adaptive Cruising Conditions," Sustainability, MDPI, vol. 17(7), pages 1-28, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3232-:d:1628367
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/7/3232/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/7/3232/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mu, Guixiang & Wei, Qingguo & Xu, Yonghong & Li, Jian & Zhang, Hongguang & Yang, Fubin & Zhang, Jian & Li, Qi, 2025. "State of health estimation of lithium-ion batteries based on feature optimization and data-driven models," Energy, Elsevier, vol. 316(C).
    2. Pan, Chaofeng & Huang, Aibao & Wang, Jian & Chen, Liao & Liang, Jun & Zhou, Weiqi & Wang, Limei & Yang, Jufeng, 2022. "Energy-optimal adaptive cruise control strategy for electric vehicles based on model predictive control," Energy, Elsevier, vol. 241(C).
    3. Zhang, Wencan & Xie, Yi & He, Hancheng & Long, Zhuoru & Zhuang, Liyang & Zhou, Jianjie, 2025. "Multi-physics coupling model parameter identification of lithium-ion battery based on data driven method and genetic algorithm," Energy, Elsevier, vol. 314(C).
    4. Zhiwen Zhang & Jie Tang & Jiyuan Zhang & Tianci Zhang, 2024. "Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle," Energies, MDPI, vol. 17(6), pages 1-22, March.
    5. Khoudir Kakouche & Adel Oubelaid & Smail Mezani & Djamila Rekioua & Toufik Rekioua, 2023. "Different Control Techniques of Permanent Magnet Synchronous Motor with Fuzzy Logic for Electric Vehicles: Analysis, Modelling, and Comparison," Energies, MDPI, vol. 16(7), pages 1-28, March.
    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. Bin Huang & Wenbin Yu & Zhuang Wu & Ansheng Yang & Jinyu Wei, 2025. "System Integration to Intelligent Control: State of the Art and Future Trends of Electric Vehicle Regenerative Braking Systems," Energies, MDPI, vol. 18(19), pages 1-45, September.

    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. Chenyuan Liu & Heng Li & Kexin Li & Yue Wu & Baogang Lv, 2025. "Deep Learning for State of Health Estimation of Lithium-Ion Batteries in Electric Vehicles: A Systematic Review," Energies, MDPI, vol. 18(6), pages 1-20, March.
    2. Zhiwen Zhang & Jie Tang & Jiyuan Zhang & Tianci Zhang, 2024. "Research on Energy Hierarchical Management and Optimal Control of Compound Power Electric Vehicle," Energies, MDPI, vol. 17(6), pages 1-22, March.
    3. Xia, Baozhou & Ye, Min & Wei, Meng & Wang, Qiao & Lian, Gaoqi & Li, Yan, 2025. "SOH estimation of lithium-ion batteries with local health indicators in multi-stage fast charging protocols," Energy, Elsevier, vol. 334(C).
    4. Li, Jiajia & Yi, Qian & Zhu, Pengxing & Hu, Jianjun & Yi, Shuping, 2025. "Data-driven co-optimization method of eco-adaptive cruise control for plug-in hybrid electric vehicles considering risky driving behaviors," Applied Energy, Elsevier, vol. 392(C).
    5. Zhang, Fengqi & Qi, Zhicheng & Xiao, Lehua & Coskun, Serdar & Xie, Shaobo & Liu, Yongtao & Li, Jiacheng & Song, Ziyou, 2025. "Co-optimization on ecological adaptive cruise control and energy management of automated hybrid electric vehicles," Energy, Elsevier, vol. 314(C).
    6. Liu, Wei & Teh, Jiashen & Alharbi, Bader, 2025. "An asynchronous electro-thermal coupling modeling method of lithium-ion batteries under dynamic operating conditions," Energy, Elsevier, vol. 324(C).
    7. Li, Yang & Gao, Guoqiang & Chen, Kui & He, Shuhang & Liu, Kai & Xin, Dongli & Luo, Yang & Long, Zhou & Wu, Guangning, 2025. "State-of-health prediction of lithium-ion batteries using feature fusion and a hybrid neural network model," Energy, Elsevier, vol. 319(C).
    8. Chen, Xingyuan & Hu, Yang & Zhao, Jingwei & Wang, Yini, 2025. "Downscaling deconstruction, hybrid semi-mechanism state estimation and cascaded dynamic equivalent modelling of complex district heating networks," Energy, Elsevier, vol. 322(C).
    9. Xia, Xuelei & Chen, Yang & Shen, Jiangwei & Liu, Yonggang & Zhang, Yuanjian & Chen, Zheng & Wei, Fuxing, 2025. "State of health estimation for lithium-ion batteries based on impedance feature selection and improved support vector regression," Energy, Elsevier, vol. 326(C).
    10. Wang, Zhuoer & Zhu, Xiaowen & Wang, Qingbo & Zhou, Jian & Li, Bijun & Shi, Baohan & Zhang, Chenming, 2025. "MapVC: Map-based deep learning for real-time current prediction in eco-driving electric vehicles," Applied Energy, Elsevier, vol. 396(C).
    11. Wang, Yaxuan & Guo, Shilong & Cui, Yue & Deng, Liang & Zhao, Lei & Li, Junfu & Wang, Zhenbo, 2025. "A comprehensive review of machine learning-based state of health estimation for lithium-ion batteries: data, features, algorithms, and future challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 224(C).
    12. Jingrui Liu & Zhiwen Hou & Bowei Liu & Xinhui Zhou, 2025. "Mathematical and Machine Learning Innovations for Power Systems: Predicting Transformer Oil Temperature with Beluga Whale Optimization-Based Hybrid Neural Networks," Mathematics, MDPI, vol. 13(11), pages 1-34, May.
    13. Yang, Li & He, Mingjian & Ren, Yatao & Gao, Baohai & Qi, Hong, 2025. "Physics-informed neural network for co-estimation of state of health, remaining useful life, and short-term degradation path in Lithium-ion batteries," Applied Energy, Elsevier, vol. 398(C).
    14. Li, Wei & Xu, Shengguan & Chen, Yujie & Wang, Bohong & Wang, Qiuwang, 2025. "Numerical insight into characteristics and performance of zinc-bromine redox flow battery," Energy, Elsevier, vol. 335(C).
    15. Bin Huang & Wenbin Yu & Zhuang Wu & Ansheng Yang & Jinyu Wei, 2025. "System Integration to Intelligent Control: State of the Art and Future Trends of Electric Vehicle Regenerative Braking Systems," Energies, MDPI, vol. 18(19), pages 1-45, September.
    16. Djamel Ikni & Elena Raducan, 2024. "Stability Analysis of a Wind Turbine Controlled by Direct Torque Control," Energies, MDPI, vol. 17(11), pages 1-20, May.
    17. Jia, Xingyun & Zhou, Dengji, 2024. "Multi-variable anti-disturbance controller with state-dependent switching law for adaptive cycle engine," Energy, Elsevier, vol. 288(C).
    18. Wang, Zhao & Qin, Yanyan, 2025. "A Nash optimal distributed model predictive control for heavy-duty truck platoon to enhance operation and fuel-saving under freeway cut-in conditions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 670(C).
    19. Li, Yuxi & Zhang, Yu & Hao, Gang, 2025. "Stochastic energy model predictive control for cooperative adaptive cruising," Energy, Elsevier, vol. 334(C).
    20. Wang, Yechen & Zhang, Xiangwen & Chen, Deqi & Yang, Jiangang, 2025. "State of health estimation of lithium-ion batteries based on BKA-FSVR algorithm with feature reconstruction from partial constant current charging interval," Energy, Elsevier, vol. 335(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:gam:jsusta:v:17:y:2025:i:7:p:3232-:d:1628367. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.