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Smart energy management for hybrid electric bus via improved soft actor-critic algorithm in a heuristic learning framework

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  • Huang, Ruchen
  • He, Hongwen
  • Su, Qicong

Abstract

Deep reinforcement learning (DRL) is currently the cutting-edge artificial intelligence approach in the field of energy management for hybrid electric vehicles. However, inefficient offline training limits the energy-saving efficacy of DRL-based energy management strategies (EMSs). Motivated by this, this article proposes a smart DRL-based EMS in a heuristic learning framework for an urban hybrid electric bus. In order to enhance the sampling efficiency, the prioritized experience replay technique is introduced into soft actor-critic (SAC) for the innovative formulation of an improved SAC algorithm. Additionally, to strengthen the generalizability of the improved SAC agent to real driving scenarios, a stochastic training environment is constructed. Afterward, curriculum learning is employed to develop a heuristic learning framework that expedites convergence. Experimental simulations reveal that the designed EMS expedites convergence by 85.58 % and saves fuel by 6.43 % compared with the cutting-edge baseline EMS. Moreover, the computation complexity test demonstrates that the designed EMS holds significant promise for real-time implementation. These findings highlight the contribution of this article in facilitating fuel conservation for urban hybrid electric buses through the application of emerging artificial intelligence technologies.

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  • Huang, Ruchen & He, Hongwen & Su, Qicong, 2024. "Smart energy management for hybrid electric bus via improved soft actor-critic algorithm in a heuristic learning framework," Energy, Elsevier, vol. 309(C).
    • Handle: RePEc:eee:energy:v:309:y:2024:i:c:s0360544224028664
    DOI: 10.1016/j.energy.2024.133091
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    References listed on IDEAS

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