基于阻容参数滤波优化UKF的锂电池SOC估计  

SOC Estimation of Lithium Battery Based on Resistance-capacitance Parameters Filtering Optimization UKF

作　　者：胡劲 赵靖英[1] 姚帅亮 张文煜 HU Jin;ZHAO Jingying;YAO Shuailiang;ZHANG Wenyu(State Key Laboratory of Reliability and Intelligence of Electrical Equipment,College of Electrical Engineering,Hebei University of Technology,Tianjin 300130,China;State Grid Jibei Zhangjiakou Wind-PV-Storage-Transportation New Energy Co.,Ltd.,Zhangjiakou 075000,China)

机构地区：[1]河北工业大学电气工程学院,省部共建电工装备可靠性与智能化国家重点实验室,天津300130 [2]国网冀北张家口风光储输新能源有限公司,张家口075000

出　　处：《电源学报》2025年第2期247-255,共9页Journal of Power Supply

基　　金：国家自然科学基金重点资助项目(5137704);河北省自然科学基金资助项目(E2019202481,E2017202284)。

摘　　要：锂电池荷电状态SOC(state-of-charge)的快速精确估计,对电池管理系统至关重要。针对卡尔曼滤波算法估计锂电池SOC时阻容参数缺乏合理约束的问题,提出1种阻容参数滤波优化方法,结合无迹卡尔曼滤波UKF(unscented Kalman filter)实现锂电池SOC估计的快速精确收敛。首先,结合多项式建立锂电池等效电路模型;然后,利用带遗忘因子的递推最小二乘法获取时变和时不变的模型阻容参数,通过设置卡尔曼增益阈值,建立阻容参数滤波关系式,提出阻容参数滤波优化无迹卡尔曼滤波算法,估计锂电池SOC;最后,设计混合功率脉冲特性实验、间歇恒流放电实验和动应力测试实验,验证设计方法的收敛性和鲁棒性,SOC最大估计误差低于1.0%,并给出增益阈值参考范围。A fast and accurate estimation of the state-of-charge(SOC)of lithium batteries is critical for the battery management system.Aimed at the problem that the Kalman filter algorithm lacks reasonable constraints on the resistance-capacitance(RC)parameters when estimating the SOC of lithium batteries,an optimization method of RC parameters filtering is proposed,and it is combined with unscented Kalman filter(UKF)to achieve the fast and accurate convergence of lithium battery SOC estimation.First,an equivalent circuit model of lithium battery is established by combing the polynomial equation.Then,forgetting factor recursive least squares is used to obtain the time-varying and time-invariant model RC parameters.The expression of RC parameters filtering relationship is established by setting the Kalman gain threshold,and an RC optimization UKF algorithm is proposed for lithium battery SOC estimation.Finally,hybrid pulse-power characteristic experiment,intermittent constant-current discharge experiment and dynamic stress test experiment were designed to verify the convergence and robustness of the proposed algorithm.The maximum estimation error of SOC was less than 1.0%,and the reference range of gain threshold was also given.

关 键 词：锂电池 荷电状态 阻容参数 无迹卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]