基于改进递推最小二乘法的锂电池SOC估算  

SOC Estimation of Lithium Batteries Based on Improved Recursive Least Squares Method

作　　者：虞杨 郑燕萍[2] YU Yang;ZHENG Yan-ping(College of Mechanical and Electronic Engineering,Nanjing Forestry University,Nanjing 210037,China;College of Automobile and Traffic Engineering,Nanjing Forestry University,Nanjing 210037,China)

机构地区：[1]南京林业大学机械电子工程学院,江苏南京210037 [2]南京林业大学汽车与交通工程学院,江苏南京210037

出　　处：《控制工程》2021年第9期1759-1764,共6页Control Engineering of China

基　　金：2017年江苏省重点研发计划项目(BE2017008)。

摘　　要：针对电动汽车锂离子电池荷电状态估算问题,提出了一种基于改进递推最小二乘法的三元锂电池SOC估算算法。首先建立三元锂电池的一阶RC等效电路模型,然后在递推最小二乘法的基础上,考虑对待估参数的动态约束和回溯,对模型参数进行在线辨识,与扩展卡尔曼滤波(EKF)结合来估算电池SOC,最后对三元锂电池进行动态工况仿真试验。结果表明,改进后的递推最小二乘法能够对参数在复杂工况下的异常抖动进行有效抑制,减小端电压预测误差,将其与EKF结合估算电池SOC可以获得更高的精度。In view of estimating the state of charge(SOC) of lithium batteries in electric vehicles, SOCestimation algorithm of a ternary lithium battery based on improved recursive least squares method is proposed. Firstly, a first-order RC equivalent circuit model of a ternary lithium battery is established. Then, based on the recursive least squares method, the dynamic constraints and backtracking of the parameters to be evaluated are considered, the model parameters are identified online, and extended Kalman filter(EKF) is combined to estimate SOC. Finally, a dynamic operating condition simulation test is performed on the ternary lithium battery. The results show that the improved recursive least squares method can effectively suppress the abnormal jitter of the parameters in complex operating conditions, and reduce the terminal voltage prediction error. Combining the method with EKF to estimate SOC can achieve higher accuracy.

关 键 词：三元锂电池 改进递推最小二乘法 在线参数辨识 联合估算 

分 类 号：TM912.9[电气工程—电力电子与电力传动]