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Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle

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  • Lian, Renzong
  • Peng, Jiankun
  • Wu, Yuankai
  • Tan, Huachun
  • Zhang, Hailong

Abstract

The optimization and training processes of deep reinforcement learning (DRL) based energy management strategy (EMS) can be very slow and resource-intensive. In this paper, an improved energy management framework that embeds expert knowledge into deep deterministic policy gradient (DDPG) is proposed. Incorporated with the battery characteristics and the optimal brake specific fuel consumption (BSFC) curve of hybrid electric vehicles (HEVs), we are committed to solving the optimization problem of multi-objective energy management with a large space of control variables. By incorporating this prior knowledge, the proposed framework not only accelerates the learning process, but also gets a better fuel economy, thus making the energy management system relatively stable. The experimental results show that the proposed EMS outperforms the one without prior knowledge and the other state-of-art deep reinforcement learning approaches. In addition, the proposed approach can be easily generalized to other types of HEV EMSs.

Suggested Citation

  • Lian, Renzong & Peng, Jiankun & Wu, Yuankai & Tan, Huachun & Zhang, Hailong, 2020. "Rule-interposing deep reinforcement learning based energy management strategy for power-split hybrid electric vehicle," Energy, Elsevier, vol. 197(C).
    • Handle: RePEc:eee:energy:v:197:y:2020:i:c:s0360544220304047
    DOI: 10.1016/j.energy.2020.117297
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    References listed on IDEAS

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