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

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  • 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
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    References listed on IDEAS

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    1. 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.
    2. 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).
    3. 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.
    4. 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).
    5. 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).
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    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.

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