摘要
基于锂离子电池的集总参数模型,建立产热内阻随温度、SOC和循环次数变化的数学模型,并提出了一种基于带遗忘因子的递推最小二乘法(recursive least square with forgetting factor,FFRLS)-无迹卡尔曼滤波算法(unscented Kalman filter,UKF)的锂离子电池内核温度估计方法。在单周期的FUDS工况下进行实验验证,根据采集的电池表面温度值、环境温度和电流值在线辨识热阻容参数和内阻,并验证所辨识参数的准确性;用FFRLS-UKF算法估计内核温度的变化,实验结果表明,该方法可以很快消除未知初始值的误差,并跟随到真值附近,最大误差在1℃以内。在此基础上设计了150次FUDS循环充放电的仿真实验,仿真结果表明,在循环工况下,该方法能够较好地追踪内核温度和内阻平均值变化情况,显示了较好的稳定性和鲁棒性。
Based on the lumped parameter model of lithium ion battery,the mathematical model of the internal resistance of heat generation with temperature,SOC and cycle number is established.A method of temperature estimation of lithium ion battery core based on recursive least square with forgetting factor(FFRLS)-unscented Kalman filter(UKF)is proposed.In the single cycle FUDS working condition,the thermal parameters and internal resistance are identified online according to the collected battery surface temperature,ambient temperature and current value,and the accuracy of the identified parameters is verified.The change of the core temperature is estimated by FFRLS-UKF algorithm.The experimental results show that the method can quickly eliminate the error of unknown initial value,and follow the true value and the maximum error is within 1℃.On this basis,150 cycles of FUDS charge and discharge simulation experiments are designed.The simulation results show that the method can track the changes of the core temperature and internal resistance under the cycle conditions,which exhibits good stability and robustness.
作者
王萍
张吉昂
程泽
WANG Ping;ZHANG Ji'ang;CHENG Ze(School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China)
出处
《电源技术》
CAS
北大核心
2021年第11期1458-1462,共5页
Chinese Journal of Power Sources
基金
国家自然科学基金资助项目(61873180)。
关键词
锂离子电池
温度预测
参数辨识
无迹卡尔曼滤波算法
lithium ion battery
temperature prediction
parameter identification
unscented Kalman filter algorithm
