陈刚,储建新,潘炫霖,雷健新,郑迪
CHEN Gang,CHU Jianxin,PAN Xuanling,LEI Jianxin,ZHENG Di

• 1:国网浙江海盐县供电有限公司
• 2:中国计量大学

摘要(Abstract)：

锂电池的SOC(荷电状态)准确估计是电池管理系统及其控制的基础。现有扩展卡尔曼滤波、无迹卡尔曼滤波等方法需计算高维雅克比矩阵或协方差矩阵,对计算能力要求较高。结合数据同化和集合预报的思想,提出基于联合EnKF(集合卡尔曼滤波)的锂电池SOC估计方法。该方法利用集合的统计特征来表征状态变量,避免了高维矩阵的运算,对SOC和模型参数进行联合估计,可提高算法速度和精度。建立了锂电池等效电路模型并辨识了模型初始参数,得到了开路电压曲线。在EnKF的基础上,针对充放电过程中模型参数的变化,提出了基于联合EnKF的SOC估计方法,可在计算过程中联合估计SOC和模型参数。实验结果表明,所提方法可准确高效地估计锂电池的SOC。
Accurate estimation of SOC(state of charge) of lithium-ion batteries is the basis of the battery management system and its control. The existing methods such as the extended Kalman filter and unscented Kalman filter need to calculate a high-dimensional Jacobian matrix or a covariance matrix, which require a higher calculation capability. Given the ideas of data assimilation and ensemble forecasting, a lithium-ion battery SOC estimation method based on EnKF(ensemble Kalman filter) is proposed. This method represents state variables by statistical characteristics of the ensemble and avoids the operation of high-dimensional matrices; it estimates SOC and model parameters to improve operation speed and accuracy. An equivalent circuit model of the lithium-ion battery is established and the initial parameters of the model are identified to obtain an open circuit voltage curve. Based on EnKF and model parameter change during charging and discharging processes, a SOC estimation method based on the joint EnKF method is proposed, which can estimate SOC and model parameters at the same time. The experimental results prove that the proposed method can accurately and efficiently estimate the SOC of lithium-ion batteries.

关键词(KeyWords)： 锂电池;SOC;状态估计;集合卡尔曼滤波
lithium-ion battery;SOC;state estimation;EnKF

Abstract:

Keywords:

基金项目(Foundation): 浙江省自然科学基金项目(LQ20E090006)

作者(Author): 陈刚,储建新,潘炫霖,雷健新,郑迪
CHEN Gang,CHU Jianxin,PAN Xuanling,LEI Jianxin,ZHENG Di

DOI: 10.19585/j.zjdl.202101018

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