Analysis of Energy Management Strategies For Hydrogen Fuel Cell Hybrid Rail Transit

Hydrogen fuel cell hybrid vehicles have emerged as a prominent research area in the realm of rail transit. Energy management assumes a critical role in the hybrid power system, which profoundly influences the vehicle's power, economy, safety, and comfort. This paper aims to analyze existing energy management strategies in this context. Firstly, the paper elucidates the connotation, essence, and objective of energy management for hybrid trains. Subsequently, the existing research is categorized into two distinct groups: traditional energy management strategies and learning-based energy management strategies. The traditional strategies are succinctly classified and exemplified, whereas the learning-based strategies primarily focus on reinforcement learning and present a comprehensive explanation of improved reinforcement learning algorithms and their application in energy management problems. Finally, the paper concludes with a summary and offers a future outlook on reinforcement learning-based strategies.