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Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles

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  • Wang, Hao
  • He, Hongwen
  • Bai, Yunfei
  • Yue, Hongwei

Abstract

Energy management strategy (EMS) is an essential technique to ensure the long- term driving economy of hybrid electric vehicles (HEVs). The complicated discrete–continuous hybrid action space lying in HEV’s driving system presents a challenge to achieve high-performance EMSs. Thus, this paper proposes a novel improved deep Q-network (DQN)-based EMS to reduce the HEV’s driving costs, with lithium-ion battery (LIB) life and energy economy considered. Firstly, a data-driven battery life map reflecting the non-linear decaying trajectory of battery state of health (SOH) is proposed to quantify the real-time battery aging. Secondly, in the proposed EMS incorporating the battery aging model, an enhanced parameterized DQN (PDQN) algorithm is applied to particularly provide a hybrid solution discriminating between discrete and continuous actions. Finally, with the dynamic programming (DP) method employed as the benchmark, the effectiveness and optimality of the proposed EMS are validated. Without the prior knowledge of testing driving conditions, the proposed EMS effectively achieves 99.5% performance of the DP method, reducing the vehicle’s driving costs by 3.1% and extending battery life effectively. The EMS converges quickly during training and a hardware-in-loop test validates its real application potential.

Suggested Citation

  • Wang, Hao & He, Hongwen & Bai, Yunfei & Yue, Hongwei, 2022. "Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles," Applied Energy, Elsevier, vol. 320(C).
  • Handle: RePEc:eee:appene:v:320:y:2022:i:c:s0306261922006274
    DOI: 10.1016/j.apenergy.2022.119270
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    Cited by:

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    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. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    5. Ji, Jie & Zhou, Mengxiong & Guo, Renwei & Tang, Jiankang & Su, Jiaoyue & Huang, Hui & Sun, Na & Nazir, Muhammad Shahzad & Wang, Yaodong, 2023. "A electric power optimal scheduling study of hybrid energy storage system integrated load prediction technology considering ageing mechanism," Renewable Energy, Elsevier, vol. 215(C).
    6. Hu, Dong & Huang, Chao & Yin, Guodong & Li, Yangmin & Huang, Yue & Huang, Hailong & Wu, Jingda & Li, Wenfei & Xie, Hui, 2024. "A transfer-based reinforcement learning collaborative energy management strategy for extended-range electric buses with cabin temperature comfort consideration," Energy, Elsevier, vol. 290(C).
    7. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(C).
    8. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    9. Ren, Xiaoxia & Ye, Jinze & Xie, Liping & Lin, Xinyou, 2024. "Battery longevity-conscious energy management predictive control strategy optimized by using deep reinforcement learning algorithm for a fuel cell hybrid electric vehicle," Energy, Elsevier, vol. 286(C).

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