- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | YANG Jibin, HU Huaixiang, XU Xiaohui, ZHANG Jiye, JIANG Ping, WU Xiaohua, DENG Pengyi, WANG Wenlong. Energy Management Strategy for Fuel Cell Buses Integrating Speed Prediction and Reinforcement Learning[J]. Journal of Southwest Jiaotong University. doi: 10.3969/j.issn.0258-2724.20250630
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To address the difficulty of balancing fuel economy and durability, an energy management strategy (SAC-V) for fuel cell hybrid electric buses was proposed by combining a bidirectional long short-term memory neural network (BiLSTMNN) with a soft actor-critic (SAC) algorithm. First, BiLSTMNN was applied to achieve short-term vehicle speed prediction. Second, the predicted speed and real-time vehicle states were jointly used as inputs to the SAC reinforcement learning agent, and constraint terms including hydrogen consumption, power battery state of charge deviation, and fuel cell degradation were introduced into the reward function to achieve dynamic coordinated optimization of vehicle fuel economy and powertrain durability. Finally, the proposed strategy was validated through offline simulation and hardware-in-the-loop tests. The research results show that, compared with the conventional SAC method and the deep deterministic policy gradient method, the proposed SAC-V strategy reduces equivalent hydrogen consumption, fuel cell power fluctuation, and fuel cell degradation rate by 3.46%, 35.56%, and 3.67%, respectively, exhibiting better overall performance and favorable real-time application potential in practical engineering.
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