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Deep reinforcement learning based energymanagement strategy considering running costs and energy source aging for fuel cell hybrid electric vehicle

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  • Huang, Yin
  • Kang, Zehao
  • Mao, Xuping
  • Hu, Haoqin
  • Tan, Jiaqi
  • Xuan, Dongji

Abstract

The main contribution of this study is to integrate energy source aging and running costs into the deep reinforcement learning (DRL) based EMS of fuel cell hybrid electric vehicles (FCHEV). For the FCHEV, a multi-objective energy management strategy (EMS) based on twin delayed deep deterministic policy gradient (TD3) is proposed, which aims to simultaneously reduce energy source degradation and lower running costs. To achieve this, the paper innovatively designs the reward function and it's comparative approach. Additionally, it verifies the superiority of the proposed EMS over other EMS based on continuous action space algorithm, including previous action guided deep deterministic policy gradient (PA-DDPG) and soft actor-critic (SAC). Lastly, the agent's action output is changed from fuel cell (FC) current to FC power ratio, and a comparative analysis on results generated by different action outputs is conducted. Simulation results show that the proposed EMS can reduce the running costs while extending the lifespan of battery and FC efficiently. This work holds significant practical significance in the energy distribution of automobiles.

Suggested Citation

  • Huang, Yin & Kang, Zehao & Mao, Xuping & Hu, Haoqin & Tan, Jiaqi & Xuan, Dongji, 2023. "Deep reinforcement learning based energymanagement strategy considering running costs and energy source aging for fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223025719
    DOI: 10.1016/j.energy.2023.129177
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    1. Wu, Jingda & He, Hongwen & Peng, Jiankun & Li, Yuecheng & Li, Zhanjiang, 2018. "Continuous reinforcement learning of energy management with deep Q network for a power split hybrid electric bus," Applied Energy, Elsevier, vol. 222(C), pages 799-811.
    2. Maino, Claudio & Misul, Daniela & Musa, Alessia & Spessa, Ezio, 2021. "Optimal mesh discretization of the dynamic programming for hybrid electric vehicles," Applied Energy, Elsevier, vol. 292(C).
    3. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    4. Chen, Huicui & Pei, Pucheng & Song, Mancun, 2015. "Lifetime prediction and the economic lifetime of Proton Exchange Membrane fuel cells," Applied Energy, Elsevier, vol. 142(C), pages 154-163.
    5. Li, Weihan & Cui, Han & Nemeth, Thomas & Jansen, Jonathan & Ünlübayir, Cem & Wei, Zhongbao & Feng, Xuning & Han, Xuebing & Ouyang, Minggao & Dai, Haifeng & Wei, Xuezhe & Sauer, Dirk Uwe, 2021. "Cloud-based health-conscious energy management of hybrid battery systems in electric vehicles with deep reinforcement learning," Applied Energy, Elsevier, vol. 293(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. Kofinas, P. & Dounis, A.I. & Vouros, G.A., 2018. "Fuzzy Q-Learning for multi-agent decentralized energy management in microgrids," Applied Energy, Elsevier, vol. 219(C), pages 53-67.
    8. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    9. Wenz, Klaus-Peter & Serrano-Guerrero, Xavier & Barragán-Escandón, Antonio & González, L.G. & Clairand, Jean-Michel, 2021. "Route prioritization of urban public transportation from conventional to electric buses: A new methodology and a study of case in an intermediate city of Ecuador," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    10. Zhou, Yang & Ravey, Alexandre & Péra, Marie-Cecile, 2020. "Multi-mode predictive energy management for fuel cell hybrid electric vehicles using Markov driving pattern recognizer," Applied Energy, Elsevier, vol. 258(C).
    11. Li, Yuecheng & He, Hongwen & Khajepour, Amir & Wang, Hong & Peng, Jiankun, 2019. "Energy management for a power-split hybrid electric bus via deep reinforcement learning with terrain information," Applied Energy, Elsevier, vol. 255(C).
    12. Zhou, Jianhao & Xue, Siwu & Xue, Yuan & Liao, Yuhui & Liu, Jun & Zhao, Wanzhong, 2021. "A novel energy management strategy of hybrid electric vehicle via an improved TD3 deep reinforcement learning," Energy, Elsevier, vol. 224(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Jiang, Yuzheng & Dong, Jun & Huang, Hexiang, 2024. "Optimal bidding strategy for the price-maker virtual power plant in the day-ahead market based on multi-agent twin delayed deep deterministic policy gradient algorithm," Energy, Elsevier, vol. 306(C).
    2. Huang, Xuejin & Zhang, Jingyi & Ou, Kai & Huang, Yin & Kang, Zehao & Mao, Xuping & Zhou, Yujie & Xuan, Dongji, 2024. "Deep reinforcement learning-based health-conscious energy management for fuel cell hybrid electric vehicles in model predictive control framework," Energy, Elsevier, vol. 304(C).

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