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Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method

Author

Listed:
  • Ke Song

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    National Fuel Cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China)

  • Yimin Wang

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    National Fuel Cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China)

  • Cancan An

    (Beijing Aerospace Propulsion Institute, Fengtai District, Beijing 100076, China)

  • Hongjie Xu

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    National Fuel Cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China)

  • Yuhang Ding

    (School of Automotive Studies, Tongji University, Shanghai 201804, China
    National Fuel Cell Vehicle and Powertrain System Engineering Research Center, Tongji University, Shanghai 201804, China)

Abstract

In view of the aggravation of global pollution and greenhouse effects, fuel cell electric vehicles (FCEVs) have attracted increasing attention, owing to their ability to release zero emissions. Extended-range fuel cell vehicles (E-RFCEVs) are the most widely used type of fuel cell vehicles. The powertrain system of E-RFCEV is relatively complex. Bond graph theory was used to model the important parts of the E-RFCEV powertrain system: Battery, motor, fuel cell, DC/DC, vehicle, and driver. In order to verify the control effect of energy management strategy (EMS) in a real-time state, bond graph theory was applied to hardware-in-the-loop (HiL) development. An HiL simulation test-bed based on the bond graph model was built, and the HiL simulation verification of the energy management strategy was completed. Based on the comparison to a power-following EMS, it was found that fuzzy logic EMS is more adaptive to vehicle driving conditions. This study aimed to apply bond graph theory to HiL simulations to verify that bond graph modeling is applicable to complex systems.

Suggested Citation

  • Ke Song & Yimin Wang & Cancan An & Hongjie Xu & Yuhang Ding, 2021. "Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method," Energies, MDPI, vol. 14(2), pages 1-31, January.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:2:p:380-:d:478865
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    References listed on IDEAS

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    1. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2020. "Energy management strategies comparison for a parallel full hybrid electric vehicle using Reactivity Controlled Compression Ignition combustion," Applied Energy, Elsevier, vol. 272(C).
    2. Koubaa, Rayhane & krichen, Lotfi, 2017. "Double layer metaheuristic based energy management strategy for a Fuel Cell/Ultra-Capacitor hybrid electric vehicle," Energy, Elsevier, vol. 133(C), pages 1079-1093.
    3. 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.
    4. Zhang, Zhendong & He, Hongwen & Guo, Jinquan & Han, Ruoyan, 2020. "Velocity prediction and profile optimization based real-time energy management strategy for Plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 280(C).
    5. Ren, Lei & Zhou, Sheng & Ou, Xunmin, 2020. "Life-cycle energy consumption and greenhouse-gas emissions of hydrogen supply chains for fuel-cell vehicles in China," Energy, Elsevier, vol. 209(C).
    6. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    7. Deng, Zhihua & Chen, Qihong & Zhang, Liyan & Zong, Yi & Zhou, Keliang & Fu, Zhichao, 2020. "Control oriented data driven linear parameter varying model for proton exchange membrane fuel cell systems," Applied Energy, Elsevier, vol. 277(C).
    8. Mayyas, Abdel Ra'ouf & Kumar, Sushil & Pisu, Pierluigi & Rios, Jacqueline & Jethani, Puneet, 2017. "Model-based design validation for advanced energy management strategies for electrified hybrid power trains using innovative vehicle hardware in the loop (VHIL) approach," Applied Energy, Elsevier, vol. 204(C), pages 287-302.
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