基于加速时间预测的现代有轨电车储能系统能量管理与容量配置优化研究

doi:10.19595/j.cnki.1000-6753.tces.L70661

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Abstract Short battery life and limited spacing are key problems in the application of energy storage system for modern trams, bringing challenges to the energy management design and capacity configuration. This paper firstly analyzes the influence factors of battery life, building an life estimation model based on depth of discharge(DOD). An acceleration-time-based control strategy considering is proposed for battery-supercapacitor hybrid energy storage system, in which the actual available power of supercapacitor is calculated based on the predicted acceleration time, so as to determine the power distribution between battery and supercapacitor. The proposed strategy determines the acceleration-time -prediction window based on the periodicity of tram’s operation and takes the discharge time of supercapacitor into consideration. Both simulation and experiment verify that the proposed strategy maximizes the capacity utilization of supercapacitor, as a result of which the battery degradation is significantly reduced. In addition, this paper makes optimal capacity configurations based on the general rule-based strategy and the proposed strategy respectively to analyze the effect of control strategies on sizing results. Results show that the proposed strategy remarkably reduces the life cycle cost by decreasing supercapacitor configuration and prolongling battery life.

Key words： Modern tram hybrid energy storage acceleration-time-prediction energy management capacity configuration

Received: 20 August 2016 Published: 22 December 2017

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TM921

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	Zhu Feiqin
	Yang Zhongping
	Lin Fei
	Xia Huan

Cite this article:

Zhu Feiqin,Yang Zhongping,Lin Fei等. Research on Acceleration-Time-Prediction-Based Energy Management and Optimal Sizing of Onboard Energy Storage System for Modern Trams[J]. Transactions of China Electrotechnical Society, 2017, 32(23): 158-166.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.L70661 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I23/158

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