不同温度与驾驶工况下车用锂电池SOC估算  

SOC Estimation of Vehicle Lithium Battery at Different Temperatures and Driving Conditions

作　　者：杨超 何锋[1] 王文亮 YANG Chao;HE Feng;WANG Wenliang(College of Mechanical Engineering,Guizhou University,Guiyang 550025,China)

机构地区：[1]贵州大学机械工程学院,贵阳550025

出　　处：《重庆理工大学学报（自然科学）》2021年第12期47-54,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：贵州省科技支撑计划项目(黔科合支撑[2021]一般536)。

摘　　要：针对单一卡尔曼滤波(KF)在估算荷电状态(SOC)时忽略了温度、SOC以及驾驶工况对电池参数的影响,且存在收敛性差、误差大等问题,提出了一种扩展卡尔曼滤波(EKF)-自适应无迹卡尔曼滤波(AUKF)联合算法。基于电池的外特性机理建立了2阶RC等效电路模型,在OCV-SOC-T函数映射关系下,利用EKF算法实时预测电池参数,并联立AUKF算法实现SOC的估算。通过在不同温度与驾驶工况下的电池实验数据验证,EKF-AUKF联合算法能够实现电池参数和SOC的实时在线估计,同时兼顾了鲁棒性强、收敛性好以及估算精度高等特点,其估算结果明显优于单一的AUKF算法。The influence of temperature,SOC and driving condition on battery parameters is ignored when single Kalman filter(KF)is used to estimate state of charge(SOC),and there are some problems such as poor convergence and large error.A joint algorithm of extended Kalman filter(EKF)and adaptive unscented Kalman filter(AUKF)is proposed.Based on the external characteristic mechanism of the battery,a second-order RC equivalent circuit model is established.Under the mapping relationship of OCV-SOC-T function,the EKF algorithm is used to predict the battery parameters in real time,and the AUKF algorithm is used to estimate the SOC.Through the verification of the battery experimental data under different temperatures and driving conditions,the EKF-AUKF joint algorithm can realize the real-time online estimation of battery parameters and SOC.At the same time,the algorithm takes into account the characteristics of strong robustness,good convergence and high estimation accuracy,and its estimation result is obviously better than the single AUKF algorithm.

关 键 词：荷电状态 电池参数 联合算法 鲁棒性 估算精度 

分 类 号：TM912[电气工程—电力电子与电力传动]