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Bidirectional DC-DC Converter Topologies for Hybrid Energy Storage Systems in Electric Vehicles: A Comprehensive Review

Author

Listed:
  • Yan Tong

    (Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China)

  • Issam Salhi

    (FEMTO-ST Institute, CNRS, UTBM, Université Marie et Louis Pasteur, F-90000 Belfort, France)

  • Qin Wang

    (Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, China
    Shenzhen Research Institute, The Hong Kong Polytechnic University, Shenzhen 518000, China)

  • Gang Lu

    (State Grid Energy Research Institute, Beijing 102209, China)

  • Shengyu Wu

    (State Grid Energy Research Institute, Beijing 102209, China)

Abstract

Electric Vehicles (EV) significantly contribute to reducing carbon emissions and promoting sustainable transportation. Among EV technologies, hybrid energy storage systems (HESS), which combine fuel cells, power batteries, and supercapacitors, have been widely adopted to enhance energy density, power density, and system efficiency. Bidirectional DC-DC converters are pivotal in HESS, enabling efficient energy management, voltage matching, and bidirectional energy flow between storage devices and vehicle systems. This paper provides a comprehensive review of bidirectional DC-DC converter topologies for EV applications, which focuses on both non-isolated and isolated designs. Non-isolated topologies, such as Buck-Boost, Ćuk, and interleaved converters, are featured for their simplicity, efficiency, and compactness. Isolated topologies, such as dual active bridge (DAB) and push-pull converters, are featured for their high voltage gain and electrical isolation. An evaluation framework is proposed, incorporating key performance metrics such as voltage stress, current stress, power density, and switching frequency. The results highlight the strengths and limitations of various converter topologies, offering insights into their optimization for EV applications. Future research directions include integrating wide-bandgap devices, advanced control strategies, and novel topologies to address challenges such as wide voltage gain, high efficiency, and compact design. This work underscores the critical role of bidirectional DC-DC converters in advancing energy-efficient and sustainable EV technologies.

Suggested Citation

  • Yan Tong & Issam Salhi & Qin Wang & Gang Lu & Shengyu Wu, 2025. "Bidirectional DC-DC Converter Topologies for Hybrid Energy Storage Systems in Electric Vehicles: A Comprehensive Review," Energies, MDPI, vol. 18(9), pages 1-29, May.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:9:p:2312-:d:1647775
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    References listed on IDEAS

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    1. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2023. "A novel health-aware deep reinforcement learning energy management for fuel cell bus incorporating offline high-quality experience," Energy, Elsevier, vol. 282(C).
    2. Jia, Chunchun & Li, Kunang & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao, 2023. "Health-aware energy management strategy for fuel cell hybrid bus considering air-conditioning control based on TD3 algorithm," Energy, Elsevier, vol. 283(C).
    3. Jia, Chunchun & He, Hongwen & Zhou, Jiaming & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Learning-based model predictive energy management for fuel cell hybrid electric bus with health-aware control," Applied Energy, Elsevier, vol. 355(C).
    4. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang, 2024. "Health-conscious deep reinforcement learning energy management for fuel cell buses integrating environmental and look-ahead road information," Energy, Elsevier, vol. 290(C).
    5. Jia, Chunchun & Zhou, Jiaming & He, Hongwen & Li, Jianwei & Wei, Zhongbao & Li, Kunang & Shi, Man, 2023. "A novel energy management strategy for hybrid electric bus with fuel cell health and battery thermal- and health-constrained awareness," Energy, Elsevier, vol. 271(C).
    6. Songyan Niu & Qingyu Zhao & Haibiao Chen & Hang Yu & Shuangxia Niu & Linni Jian, 2022. "Underwater Wireless Charging System of Unmanned Surface Vehicles with High Power, Large Misalignment Tolerance and Light Weight: Analysis, Design and Optimization," Energies, MDPI, vol. 15(24), pages 1-19, December.
    7. Lu, Dagang & Yi, Fengyan & Hu, Donghai & Li, Jianwei & Yang, Qingqing & Wang, Jing, 2023. "Online optimization of energy management strategy for FCV control parameters considering dual power source lifespan decay synergy," Applied Energy, Elsevier, vol. 348(C).
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