IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v402y2026ipcs0306261925017350.html

A multi-criteria evaluation framework for adaptability of hybrid energy storage system energy management strategies to dynamic driving style

Author

Listed:
  • Hu, Lin
  • Zhang, Dongjie
  • Huang, Jing
  • Tian, Qingtao
  • Berecibar, Maitane
  • Zou, Changfu

Abstract

Incorporating driving style can substantially enhance the adaptability of energy management strategies in complex urban traffic, however, comprehensively and effectively evaluating different strategies remains challenging. To address this gap, this study proposes an evaluation framework based on multi-criteria decision making (MCDM), incorporating driving style characteristics for optimal adaptability of hybrid energy storage system (HESS) control strategies. Utilizing real-world urban driving data and driving styles classification, a structured indicator system is established covering system stability, battery health, efficiency, and economy. A hybrid analytic hierarchy process (AHP) and grey relational analysis (GRA) method combines expert judgment with data-driven analysis to assign indicator weights and compute comprehensive scores. Simulation comparisons show that different strategies exhibit different performance across driving scenarios, within the proposed framework, logic threshold control (LTC) and LTC with genetic algorithm (LTC-GA) perform best under conservative and standard driving styles, respectively, whereas wavelet packet transform with GA (WPT-GA) performs best under aggressive driving for its advantage in enhancing system stability through power smoothing. By comprehensively quantifying strategy adaptability, the framework provides a rigorous basis for benchmarking and for designing personalized, flexible energy management strategies.

Suggested Citation

  • Hu, Lin & Zhang, Dongjie & Huang, Jing & Tian, Qingtao & Berecibar, Maitane & Zou, Changfu, 2026. "A multi-criteria evaluation framework for adaptability of hybrid energy storage system energy management strategies to dynamic driving style," Applied Energy, Elsevier, vol. 402(PC).
  • Handle: RePEc:eee:appene:v:402:y:2026:i:pc:s0306261925017350
    DOI: 10.1016/j.apenergy.2025.127005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261925017350
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2025.127005?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. Song, Ziyou & Hofmann, Heath & Li, Jianqiu & Han, Xuebing & Ouyang, Minggao, 2015. "Optimization for a hybrid energy storage system in electric vehicles using dynamic programing approach," Applied Energy, Elsevier, vol. 139(C), pages 151-162.
    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. Chen, Zheng & Gu, Hongji & Shen, Shiquan & Shen, Jiangwei, 2022. "Energy management strategy for power-split plug-in hybrid electric vehicle based on MPC and double Q-learning," Energy, Elsevier, vol. 245(C).
    4. Suzdaleva, Evženie & Nagy, Ivan, 2019. "Two-layer pointer model of driving style depending on the driving environment," Transportation Research Part B: Methodological, Elsevier, vol. 128(C), pages 254-270.
    5. Guo, Lingxiong & Liu, Hui & Han, Lijin & Yang, Ningkang & Liu, Rui & Xiang, Changle, 2023. "Predictive energy management strategy of dual-mode hybrid electric vehicles combining dynamic coordination control and simultaneous power distribution," Energy, Elsevier, vol. 263(PA).
    6. Sulaiman, Mohd Herwan & Mustaffa, Zuriani & Mohamed, Amir Izzani & Samsudin, Ahmad Salihin & Mohd Rashid, Muhammad Ikram, 2024. "Battery state of charge estimation for electric vehicle using Kolmogorov-Arnold networks," Energy, Elsevier, vol. 311(C).
    7. Guo, Qiuyi & Zhao, Zhiguo & Shen, Peihong & Zhan, Xiaowen & Li, Jingwei, 2019. "Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle," Energy, Elsevier, vol. 186(C).
    8. Wang, Chun & Yang, Ruixin & Yu, Quanqing, 2019. "Wavelet transform based energy management strategies for plug-in hybrid electric vehicles considering temperature uncertainty," Applied Energy, Elsevier, vol. 256(C).
    9. Tian, Xiang & Cai, Yingfeng & Sun, Xiaodong & Zhu, Zhen & Xu, Yiqiang, 2019. "An adaptive ECMS with driving style recognition for energy optimization of parallel hybrid electric buses," Energy, Elsevier, vol. 189(C).
    10. Fan, Likang & Wang, Yufei & Wei, Hongqian & Zhang, Youtong & Zheng, Pengyu & Huang, Tianyi & Li, Wei, 2022. "A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 241(C).
    11. Song, Ziyou & Li, Jianqiu & Han, Xuebing & Xu, Liangfei & Lu, Languang & Ouyang, Minggao & Hofmann, Heath, 2014. "Multi-objective optimization of a semi-active battery/supercapacitor energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 135(C), pages 212-224.
    12. Wang, Yujie & Sun, Zhendong & Chen, Zonghai, 2019. "Development of energy management system based on a rule-based power distribution strategy for hybrid power sources," Energy, Elsevier, vol. 175(C), pages 1055-1066.
    13. Beatrice, C. & Capasso, C. & Doulgeris, S. & Samaras, Z. & Veneri, O., 2024. "Hybrid storage system management for hybrid electric vehicles under real operating conditions," Applied Energy, Elsevier, vol. 354(PB).
    14. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    15. Hu, Jie & Liu, Di & Du, Changqing & Yan, Fuwu & Lv, Chen, 2020. "Intelligent energy management strategy of hybrid energy storage system for electric vehicle based on driving pattern recognition," Energy, Elsevier, vol. 198(C).
    16. Zhang, Lei & Hu, Xiaosong & Wang, Zhenpo & Sun, Fengchun & Dorrell, David G., 2018. "A review of supercapacitor modeling, estimation, and applications: A control/management perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1868-1878.
    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. Hu, Lin & Tian, Qingtao & Zou, Changfu & Huang, Jing & Ye, Yao & Wu, Xianhui, 2022. "A study on energy distribution strategy of electric vehicle hybrid energy storage system considering driving style based on real urban driving data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Liu, Weirong & Yao, Pengfei & Wu, Yue & Duan, Lijun & Li, Heng & Peng, Jun, 2025. "Imitation reinforcement learning energy management for electric vehicles with hybrid energy storage system," Applied Energy, Elsevier, vol. 378(PA).
    3. Zhang, Hao & Chen, Boli & Lei, Nuo & Li, Bingbing & Chen, Chaoyi & Wang, Zhi, 2024. "Coupled velocity and energy management optimization of connected hybrid electric vehicles for maximum collective efficiency," Applied Energy, Elsevier, vol. 360(C).
    4. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    5. Yang, Chao & Liu, Kaijia & Jiao, Xiaohong & Wang, Weida & Chen, Ruihu & You, Sixiong, 2022. "An adaptive firework algorithm optimization-based intelligent energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 239(PB).
    6. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    7. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).
    9. He, Xionglin & Yu, Qiang & Jiang, Zihong & Tan, Yilin & Chen, Yongji & Xie, Bin & Wen, Changkai, 2025. "Dual-layer adaptive power management strategy for E-tractor incorporating operating information and deep reinforcement learning," Energy, Elsevier, vol. 319(C).
    10. Zhang, Hao & Lei, Nuo & Liu, Shang & Fan, Qinhao & Wang, Zhi, 2023. "Data-driven predictive energy consumption minimization strategy for connected plug-in hybrid electric vehicles," Energy, Elsevier, vol. 283(C).
    11. Wu, Huiduo & Min, Haitao & Zhao, Honghui & Sun, Weiyi, 2025. "A novel EMS for FCV combined with condition-adaptive search ranges of decision variables based on the spatial and temporal traffic characteristics," Energy, Elsevier, vol. 335(C).
    12. Abd-Elhaleem, Sameh & Shoeib, Walaa & Sobaih, Abdel Azim, 2023. "A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm," Energy, Elsevier, vol. 265(C).
    13. Huang, Ying & Wang, Shilong & Li, Ke & Fan, Zhuwei & Xie, Haiming & Jiang, Fachao, 2023. "Multi-parameter adaptive online energy management strategy for concrete truck mixers with a novel hybrid powertrain considering vehicle mass," Energy, Elsevier, vol. 277(C).
    14. Zhu, Tao & Lot, Roberto & Wills, Richard G.A. & Yan, Xingda, 2020. "Sizing a battery-supercapacitor energy storage system with battery degradation consideration for high-performance electric vehicles," Energy, Elsevier, vol. 208(C).
    15. Hou, Jun & Song, Ziyou, 2020. "A hierarchical energy management strategy for hybrid energy storage via vehicle-to-cloud connectivity," Applied Energy, Elsevier, vol. 257(C).
    16. Liu, Huanlong & Chen, Guanpeng & Li, Dafa & Wang, Jiawei & Zhou, Jianyi, 2021. "Energy active adjustment and bidirectional transfer management strategy of the electro-hydrostatic hydraulic hybrid powertrain for battery bus," Energy, Elsevier, vol. 230(C).
    17. Angel Recalde & Ricardo Cajo & Washington Velasquez & Manuel S. Alvarez-Alvarado, 2024. "Machine Learning and Optimization in Energy Management Systems for Plug-In Hybrid Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 17(13), pages 1-39, June.
    18. Wang, Bin & Xu, Jun & Cao, Binggang & Ning, Bo, 2017. "Adaptive mode switch strategy based on simulated annealing optimization of a multi-mode hybrid energy storage system for electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 596-608.
    19. Jiajun Liu & Tianxu Jin & Li Liu & Yajue Chen & Kun Yuan, 2017. "Multi-Objective Optimization of a Hybrid ESS Based on Optimal Energy Management Strategy for LHDs," Sustainability, MDPI, vol. 9(10), pages 1-18, October.
    20. Jiaming Zhou & Chunxiao Feng & Qingqing Su & Shangfeng Jiang & Zhixian Fan & Jiageng Ruan & Shikai Sun & Leli Hu, 2022. "The Multi-Objective Optimization of Powertrain Design and Energy Management Strategy for Fuel Cell–Battery Electric Vehicle," Sustainability, MDPI, vol. 14(10), pages 1-19, May.

    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:appene:v:402:y:2026:i:pc:s0306261925017350. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.