基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算

doi:10.19595/j.cnki.1000-6753.tces.160837

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Abstract Accurate estimation of battery state of charge (SOC) is one of the core technologies of the battery management system.In order to improve accuracy of battery SOC estimation of extended Kalman filter,the grey prediction model (GM) and extended Kalman filter (EKF) are fused to build the GM-EKF algorithm and estimate battery SOC.Firstly,The GM (1,1) is used to replace the Jacobian matrix in EKF and predict the current battery system status as a priori estimate.Then,the priori estimate is updated and revised through observations to get posterior estimation and obtain the battery SOC.The simulated working condition test of battery is implemented on the self-build experiment platform.The experimental results show that GM-EKF algorithm has higher estimation accuracy comparing to EKF algorithm on battery SOC estimation,and the estimation error is less than±0.005.The research result has realistic guiding meanings on battery SOC estimation for battery management system.

Key words： Lithium-ion battery state of charge grey prediction model extended Kalman filter

Received: 02 June 2016 Published: 10 November 2017

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U463

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	Pan Haihong
	Lü Zhiqiang
	Li Junzi
	Chen Lin

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Pan Haihong,Lü Zhiqiang,Li Junzi等. Estimation of Lithium-Ion Battery State of Charge Based on Grey Prediction Model-Extended Kalman Filter[J]. Transactions of China Electrotechnical Society, 2017, 32(21): 1-8.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.160837 OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I21/1

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