基于扩展卡尔曼滤波的锂离子电池荷电状态估计

摘要/Abstract

摘要： 针对电池荷电状态（SOC）难以准确估计的问题，采用扩展卡尔曼滤波方法来提高SOC的估计精度。首先以磷酸铁锂电池为研究对象，建立了电池的PNGV等效电路模型，并采用充放电实验和离线辨识的方法得到模型中的参数，得到了开路电压、欧姆内阻、极化内阻和极化电容与SOC的多项式函数关系；然后，对模型进行验证，并分析了模型的准确性；最后，在实际工况下，运用扩展卡尔曼滤波方法估计锂离子电池的SOC值，并与安时法计算的SOC值进行比较。结果表明，PNGV模型结合扩展卡尔曼滤波方法估计的锂离子电池SOC值的最大误差仅为2.78%，提高了电池SOC的估计精度。

关键词: 锂离子电池, 荷电状态（SOC）, PNGV模型, 开路电压, 扩展卡尔曼滤波

Abstract: Aiming at the problems that it was difficult to accurately estimate SOC, the EKF method was used to improve the accuracy of SOC estimation. Firstly, the PNGV equivalent circuit models of lithium iron phosphate batteries were established, and the model parameters were obtained by charging and discharging experiments and off-line identification method. The polynomial function relationships among open circuit voltage, Ohmic internal resistance, polarization internal resistance, polarization capacitance and SOC were established. Then, the models were verified and the accuracy of the models was analyzed. Finally, under actual operation conditions, the SOCs of lithiumion batteries were estimated by EKF and compared with the results calculated by the Ampere-Hour method. The results show that the maximum errors of estimating SOC of lithium-ion batteries based on the proposed PNGV model combined with EKF are only 2.78%, which will improve the accuracy of battery SOC.

Key words: lithium-ion battery, state of charge(SOC), partnership for a new generation of vehicle（PNGV）model, open circuit voltage, extended Kalman filter（EKF）

中图分类号:

TM912

李伟1;刘伟嵬1;邓业林2. 基于扩展卡尔曼滤波的锂离子电池荷电状态估计[J]. 中国机械工程.

LI Wei1;LIU Weiwei1;DENG Yelin2. SOC Estimation for Lithium-ion Batteries Based on EKF[J]. China Mechanical Engineering.

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