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Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting

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  • Zhu, Tao
  • Wills, Richard G.A.
  • Lot, Roberto
  • Ruan, Haijun
  • Jiang, Zhihao

Abstract

The hybrid energy storage system (HESS) composed of batteries and supercapacitors (SCs) is a dual energy storage technology that can compensate for the shortcomings of a single energy storage technology acting alone. The energy management of HESS splits the power and energy demands from the electric vehicle (EV) to the battery and SC and thus is vital to EV propulsion. This paper presents an online energy management strategy (EMS) that optimises the operating costs of battery-SC HESS and can be adaptive to real-time EV driving conditions. We analyse the optimal offline benchmarks to guide online EMS design and propose the adaptive online EMS with variable perception horizon based on both neural network and rule-based techniques. Compared with existing research, the proposed EMS features reduced complexity, flexible perception and intelligent rulemaking. Case study results show that the proposed variable perception horizon and neural network fitting can improve EMS optimality compared with the conventional methods in existing research. The proposed EMS can realise more than 97% cost optimisation efficacy of offline benchmarks. By the proposed EMS, this paper is expected to provide a practical and effective energy management approach for the battery-SC HESS to reduce costs in EV applications.

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  • Zhu, Tao & Wills, Richard G.A. & Lot, Roberto & Ruan, Haijun & Jiang, Zhihao, 2021. "Adaptive energy management of a battery-supercapacitor energy storage system for electric vehicles based on flexible perception and neural network fitting," Applied Energy, Elsevier, vol. 292(C).
    • Handle: RePEc:eee:appene:v:292:y:2021:i:c:s0306261921004128
    DOI: 10.1016/j.apenergy.2021.116932
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    References listed on IDEAS

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    Cited by:

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    2. Zhangyu Lu & Xizheng Zhang, 2022. "Composite Non-Linear Control of Hybrid Energy-Storage System in Electric Vehicle," Energies, MDPI, vol. 15(4), pages 1-15, February.
    3. Elango Sangeetha & Vijayapriya Ramachandran, 2022. "Different Topologies of Electrical Machines, Storage Systems, and Power Electronic Converters and Their Control for Battery Electric Vehicles—A Technical Review," Energies, MDPI, vol. 15(23), pages 1-28, November.
    4. Abdeldjalil Djouahi & Belkhir Negrou & Boubakeur Rouabah & Abdelbasset Mahboub & Mohamed Mahmoud Samy, 2023. "Optimal Sizing of Battery and Super-Capacitor Based on the MOPSO Technique via a New FC-HEV Application," Energies, MDPI, vol. 16(9), pages 1-18, May.
    5. Badji, Abderrezak & Abdeslam, Djaffar Ould & Chabane, Djafar & Benamrouche, Nacereddine, 2022. "Real-time implementation of improved power frequency approach based energy management of fuel cell electric vehicle considering storage limitations," Energy, Elsevier, vol. 249(C).
    6. Ma, Bin & Guo, Xing & Li, Penghui, 2023. "Adaptive energy management strategy based on a model predictive control with real-time tuning weight for hybrid energy storage system," Energy, Elsevier, vol. 283(C).
    7. 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.
    8. Xiao Hu & Shikun Liu & Ke Song & Yuan Gao & Tong Zhang, 2021. "Novel Fuzzy Control Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles Considering State of Health," Energies, MDPI, vol. 14(20), pages 1-20, October.

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