Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics

Tianfeng Tang, Qianlong Peng, Qing Shi, Qingguo Peng, Jin Zhao, Chaoyi Chen and Guangwei Wang

Energy, 2024, vol. 311, issue C

Abstract: Environmental characteristics, particularly in mountainous regions with significant slopes, substantially impact vehicle power demand and fuel consumption, thereby influencing both fuel economy and vehicle lifespan. However, existing research lacks energy management strategies specifically designed for these challenging terrains. This study presents an innovative energy management strategy (EMS) for fuel cell hybrid electric buses (FCHEB) utilizing a deep reinforcement learning algorithm considering terrain characteristics. A comprehensive model that incorporates road fluctuations and steep slopes was developed to accurately represent the terrain features of mountainous regions. Driving cycle and road condition data specific to hydrogen fuel cell buses in these areas were collected and processed as for the training sets of EMSs. Subsequently, an EMS based on the Proximal Policy Optimization (PPO) algorithm was devised to address the unique characteristics of these mountainous regions. Simulation results indicate a 26.16 % improvement in fuel economy and a 44.98 % enhancement in convergence efficiency compared to traditional methods that do not consider road slopes. Further validation through new bus route trials and standard driving cycle tests confirms the strategy's robustness and adaptability.

Keywords: Energy management strategy; Fuel cell hybrid electric buses; Deep reinforcement learning; Mountainous regions (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:311:y:2024:i:c:s0360544224030895

DOI: 10.1016/j.energy.2024.133313

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