基于卡尔曼滤波的锂离子电池模型参数辨识  

Model Parameters Estimation of Lithium Batteries Based on Kalman Filtering

作　　者：项宇[1] 刘春光[1] 李嘉麒 

机构地区：[1]装甲兵工程学院陆战平台全电化技术重点实验室,北京100072

出　　处：《兵器装备工程学报》2016年第10期147-151,共5页Journal of Ordnance Equipment Engineering

摘　　要：为了提高锂离子电池仿真模型精度,提出了基于改进的粒子群优化卡尔曼滤波(IPSO-KF)算法辨识电池模型参数。依据卡尔曼滤波算法,根据电池电压、电流,估计电池二阶RC模型参数。利用改进的粒子群算法优化卡尔曼滤波中的协方差矩阵,提高模型参数的辨识精度,建立精确的电池模型。In order to improve the simulation model accuracy of lithium battery,IPSO-KF（ Improved Particle Swarm Optimization- Kalman Filter） algorithm was established to estimate the model parameters of battery. The algorithm could estimate the second order RC model parameter according to battery voltage and current data with Kalman filtering algorithm. The IPSO algorithm was used to optimize the covariance matrix of KF algorithm to improve the accuracy of model parameters and build precise battery model.

关 键 词：锂离子电池 参数辨识 粒子群优化算法 卡尔曼滤波 

分 类 号：TM921[电气工程—电力电子与电力传动]