基于自适应无迹卡尔曼滤波的动力电池健康状态检测及梯次利用研究

doi:10.19595/j.cnki.1000-6753.tces.171452

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Abstract It is essential to estimate the state of charge (SOC) and state of health (SOH) of the cell in the electric vehicle li-ion power battery accurately for extending the power battery life and the battery echelon utilization. Based on the Thevenin equivalent circuit model of battery, the adaptive unscented Kalman filter (AUKF) is used to estimate the ohmic resistance and the state of charge in real time. According to the function between the ohmic resistance and the state of health, the state of health can be estimated in real time. The charging and discharging experiments of battery under two different conditions verify the feasibility and accuracy of this method. In addition, through the estimation of SOH of the power battery pack and cells, the unqualified cell can be located, the intact rate of battery can be quantified, and a clear solution for the electric vehicle li-ion power battery echelon utilization can be formulated, which maximizes the resource utilization of waste power batteries.

Key words： Adaptive unscented Kalman filter state of charge state of health battery intact rate Li-ion power battery echelon utilization

Received: 13 October 2017 Published: 26 September 2019

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TM919

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	Yan Xiangwu
	Deng Haoran
	Guo Qi
	Qu Wei

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Yan Xiangwu,Deng Haoran,Guo Qi等. Study on the State of Health Detection of Power Batteries Based on Adaptive Unscented Kalman Filters and the Battery Echelon Utilization[J]. Transactions of China Electrotechnical Society, 2019, 34(18): 3937-3948.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.171452 OR https://dgjsxb.ces-transaction.com/EN/Y2019/V34/I18/3937

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