Reinforcement Learning Energy Management Strategy of Tram Based on Condition Identification

被引：0
|
作者：
Mo H. [1 ]
Yang Z. [1 ]
Lin F. [1 ]
Wang Y. [1 ]
An X. [1 ]
机构：
[1] School of Electrical Engineering, Beijing Jiaotong University, Beijing
来源：
Diangong Jishu Xuebao/Transactions of China Electrotechnical Society | 2021年 / 36卷 / 19期
关键词：
Energy management strategy; Hybrid energy storage system; Operating condition recognition; Reinforcement learning; Tram;
D O I：
10.19595/j.cnki.1000-6753.tces.L90124
中图分类号：
学科分类号：
摘要：
Energy storage hybrid trams use the energy storage system as the only power source, and optimize the design of energy management strategy, which can improve the running performance and economic benefits of the tram. Regarding the demand power of the tram as a Markov process, and in order to avoid the impact on the energy management strategy when the driving conditions change greatly, a reinforcement learning energy management strategy based on the recognition of the operating conditions is proposed. Based on historical driving data, the tram driving conditions are constructed and the Markov power state transition matrix under different operating conditions is obtained. Then, with the goal of minimizing the energy consumption of the hybrid energy storage system, the power allocation strategy under different working conditions is obtained through the reinforcement learning algorithm. Finally, the improved learning vector quantization (LVQ) neural network is used to recognize the current driving conditions in real time, and the control system makes real-time decisions based on the current recognized conditions and train status. Real vehicle data is used for simulation verification. The optimized strategy can reduce the energy storage system loss and can be applied to different driving conditions. Experimental verification with a 90kW hybrid energy storage platform verifies the feasibility of the strategy in practical engineering applications. © 2021, Electrical Technology Press Co. Ltd. All right reserved.
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页码：4170 / 4182
页数：12
相关论文
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