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

Ruchen Huang, Hongwen He and Qicong Su

Energy, 2024, vol. 309, issue C

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.

Keywords: Hybrid electric bus; Energy management strategy; Improved soft actor-critic; Curriculum learning; Heuristic learning framework (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:309:y:2024:i:c:s0360544224028664

DOI: 10.1016/j.energy.2024.133091

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