一种联合PID控制与扩展卡尔曼滤波的磷酸铁锂电池荷电状态估算方法  

State of Charge Estimation for LiFeO4 Battery Combining PID Control and Extended Kalman Filter

作　　者：周娟[1] 林加顺 吴乃豪 杨晓全 周专 张子尧 ZHOU Juan;LIN Jiashun;WU Naihao;YANG Xiaoquan;ZHOU Zhuan;ZHANG Ziyao(School of Electrical Engineering,China University of Mining and Technology,Xuzhou 221008,Jiangsu Province,China)

机构地区：[1]中国矿业大学电气工程学院,江苏省徐州市221008

出　　处：《电网技术》2023年第4期1623-1631,共9页Power System Technology

基　　金：江苏省研究生科研与实践创新计划(KYCX22_2539)。

摘　　要：锂离子电池作为新能源存储的载体,是执行“双碳”目标的重要助力,精确估算电池荷电状态(state of charge,SOC)能够有效辅助电池管理,进而延长电池使用寿命。针对卡尔曼滤波类算法的SOC估算效果受磷酸铁锂电池特性制约的问题,该文提出一种比例积分微分(proportional integral differential,PID)控制与扩展卡尔曼滤波(extended Kalman filter,EKF)联合方法。该方法利用PID控制原理设计SOC初值补偿策略并优化EKF算法的状态变量修正过程,可降低磷酸铁锂电池特性对算法的影响。实验结果表明,与EKF算法相比,所提方法在估算磷酸铁锂电池SOC时拥有更高的估算精度与更快的收敛速度,对电池模型误差与采样噪声表现出较强的鲁棒性。As the carrier of new energy storage,the lithium-ion battery is an important boost for the implementation of the"dual-carbon"goals.Accurately estimating the battery state of charge(SOC)effectively helps manage the battery,thereby prolonging the battery life.Aiming at the problem that the SOC estimation effect of the Kalman filter is weakened by the characteristics of the LiFeO4 batteries,this paper proposes a combining method based on the proportional integral differential control and the extended Kalman filter(PID-EKF).The proposed method designs a compensation strategy for the initial SOC value and optimizes the state variable correction process of the EKF to reduce the influence of LiFeO4 battery characteristics on the algorithm.The experimental results reveal that compared with the EKF the proposed method has better estimation accuracy and convergence speed in estimating the SOC of the lithium iron phosphate batteries,and that it exhibits stronger robustness to the system model errors and sampling noise.

关 键 词：磷酸铁锂电池 荷电状态 扩展卡尔曼滤波 比例积分微分控制 

分 类 号：TM721[电气工程—电力系统及自动化]