Estimation of Lithium-Ion Battery State of Charge Based on Grey Prediction Model-Extended Kalman Filter
Pan Haihong1,2, Lü Zhiqiang1, Li Junzi1, Chen Lin1,2
1.School of Mechanical Engineering Guangxi University Nanning 530000 China 2.Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical Engineering Guangxi University Nanning 530000 China
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.
潘海鸿, 吕治强, 李君子, 陈琳. 基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算[J]. 电工技术学报, 2017, 32(21): 1-8.
Pan Haihong, Lü Zhiqiang, Li Junzi, Chen Lin. Estimation of Lithium-Ion Battery State of Charge Based on Grey Prediction Model-Extended Kalman Filter. Transactions of China Electrotechnical Society, 2017, 32(21): 1-8.
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2017, Vol. 32 
