储能科学与技术2025，Vol.14Issue(10)：3996-4008,13.DOI:10.19799/j.cnki.2095-4239.2025.0194
基于IFFRLS-IMMUKF的商用车磷酸铁锂电池SOC估算
IFFRLS-IMMUKF-based estimation of the state of charge of lithium iron phosphate batteries for commercial vehicles
摘要
Abstract
State of charge(SOC)is a crucial parameter for characterizing the remaining capacities of electric vehicles(EVs).Accurate SOC estimation ensures EV safety and reliability.To facilitate accurate estimation of battery SOC in complex environments,the equivalent circuit model is constructed based on the characteristics of power batteries,and the equation of state(EOS)of the battery model is discretized.Further,to obtain the discretized EOS,the golden-jackal-optimization algorithm is combined with the forgetting factor recursive least square(FFRLS)algorithm to yield an improved FFRLS method for identifying the parameters of the battery model.Concurrently,the interacting multiple-model unscented Kalman filter(IMMUKF)algorithm is used to estimate the battery SOC,which is experimentally verified via dynamic stress tests(DST)and federal urban driving schedules(FUDS)at room and high temperatures.The experimental results indicate that the mean absolute error of the proposed improved IFFRLS-IMMUKF-based lithium-battery SOC-estimation method is within 0.8%and that the SOC-estimation accuracy for lithium iron phosphate batteries is high.关键词
金豺优化算法/遗忘因子递推最小二乘法/交互式多模型无迹卡尔曼滤波/荷电状态Key words
golden jackal optimization algorithm/forgetting factor recursive least square/interacting multiple model unscented Kalman filter/state of charge分类
信息技术与安全科学引用本文复制引用
吴华伟,何成泽,洪强,周小高,李明金,顾亚娟..基于IFFRLS-IMMUKF的商用车磷酸铁锂电池SOC估算[J].储能科学与技术,2025,14(10):3996-4008,13.基金项目
国家自然科学基金资助项目(52472405),湖北省自然科学基金(2024AFB219),湖北省自然科学基金创新发展联合基金项目(2024AFD042、2024AFD045),襄阳市科技计划湖北隆中实验室专项. （52472405）
