重庆大学学报Issue(1)：68-74,7.DOI:10.11835/j.issn.1000-582X.2014.01.011
自适应卡尔曼滤波法磷酸铁锂动力电池剩余容量估计
摘要
Abstract
The Kalman filter algorithm can be used to estimate the state of charge (SOC)of power batteries, however,it easily causes divergence due to uncertain of system noise and its estimation performance is affected by model.An adaptive Kalman filter algorithm is adopted to dynamically estimate SOC of lithium iron phosphate batteries for application in electric vehicles. At first, an equivalent circuit model, appropriate for SOC estimation is built after studying battery models.Then some charging and discharging experiments are carried out for parameter identification and the results are verified.At last,the adaptive Kalman filter algorithm is used on this model for on-line SOC estimation under unknown interfering noise. Simulation results show that adaptive Kalman filter method can correct SOC estimation error caused by tiny model error online,and the estimate accuracy is higher than Kalman filter method.Adaptive Kalman filter algorithm can also correct the initial error.Full-cycle test in electric vehicles proves that the algorithm is appropriate for SOC estimation of lithium iron phosphate battery.关键词
磷酸铁锂动力电池/剩余容量/状态估计/自适应/卡尔曼滤波Key words
lithium iron phosphate battery/state of charge/state estimation/adaptive/Kalman filters分类
信息技术与安全科学引用本文复制引用
刘和平,许巧巧,胡银全,袁闪闪..自适应卡尔曼滤波法磷酸铁锂动力电池剩余容量估计[J].重庆大学学报,2014,(1):68-74,7.基金项目
中央高校基本科研业务费资助项目 （）
