电源学报2025，Vol.23Issue(2)：247-255,9.DOI:10.13234/j.issn.2095-2805.2025.2.247
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
摘要
Abstract
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Key words
Lithium battery/state-of-charge(SOC)/resistance-capacitance(RC)parameters/unscented Kalman filter(UKF)分类
动力与电气工程引用本文复制引用
胡劲,赵靖英,姚帅亮,张文煜..基于阻容参数滤波优化UKF的锂电池SOC估计[J].电源学报,2025,23(2):247-255,9.基金项目
国家自然科学基金重点资助项目(5137704) （5137704）
河北省自然科学基金资助项目(E2019202481,E2017202284)This work is supported by National Natural Science Foundation of China under the grant 5137704 （E2019202481,E2017202284）
Natural Science Foundation of Hebei Province under the grant E2019202481 and E2017202284 （）
