有轨电车基于工况识别的强化学习能量管理策略

doi:10.19595/j.cnki.1000-6753.tces.L90124

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Abstract 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.

Key words： Tram hybrid energy storage system operating condition recognition reinforcement learning energy management strategy

Received: 30 June 2020

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TM921

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	Articles by authors
	Mo Haonan
	Yang Zhongping
	Lin Fei
	Wang Yu
	An Xingkun

Cite this article:

Mo Haonan,Yang Zhongping,Lin Fei等. Reinforcement Learning Energy Management Strategy of Tram Based on Condition Identification[J]. Transactions of China Electrotechnical Society, 2021, 36(19): 4170-4182.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L90124 OR https://dgjsxb.ces-transaction.com/EN/Y2021/V36/I19/4170

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