基于有限差分扩展卡尔曼滤波的锂离子电池SOC估计

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Abstract The fast and accurate estimation of state of charge(SOC) of lithium-ion battery is the key technology of battery management system. In view of this nonlinear dynamic system of lithium battery, this report, based on the second-order RC equivalent circuit model, uses recursive least squares method to rapidly estimate the model parameters, and then uses finite difference extended Kalman filter to estimate the battery state of charge. The simulation results show that, the battery model can essentially predict the dynamic voltage behavior of the lithium-ion battery and finite difference extended Kalman filtering algorithm can maintain good accuracy in the estimation process. What’s more, this model can effectively reduce the error of SOC estimation introduced by model errors.

Key words： Lithium-ion battery state of charge parameter identification finite difference extended Kalman filter

Received: 27 March 2013 Published: 16 June 2014

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TM315

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	Articles by authors
	Liu Yanli
	Dai Sheng
	Cheng Ze
	Zhu Lewei

Cite this article:

Liu Yanli,Dai Sheng,Cheng Ze等. Estimation of State of Charge of Lithium-ion Battery Based on Finite Difference Extended Kalman Filter[J]. Transactions of China Electrotechnical Society, 2014, 29(1): 221-228.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2014/V29/I1/221

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