测试科学与仪器2020，Vol.11Issue(1)：63-69,7.DOI:10.3969/j.issn.1674-8042.2020.01.008
基于模糊卡尔曼滤波器的锂电池荷电状态与健康状态预测
Lithium battery state of charge and state of health prediction based on fuzzy Kalman filtering
摘要
Abstract
This paper presents a more accurate battery state of charge (SOC)and state of health (SOH)estimation method.A lithium battery is represented by a nonlinear two-order resistance-capacitance equivalent circuit model.The model parameters are estimated by searching least square error optimization algorithm.Precisely defined by this method,the model parameters allow to accurately determine the capacity of the battery,which in turn allows to specify the SOC prediction value used as a basis for the SOH value.Application of the extended Kalman filter (EKF)removes the need of prior known initial SOC,and applying the fuzzy logic helps to eliminate the measurement and process noise.Simulation results obtained during the urban dynamometer driving schedule (UDDS)test show that the maximum error in estimation of the battery SOC is 0.66％.Battery capacity is estimate by offline updated Kalman filter,and then SOH will be predicted.The maximum error in estimation of the battery capacity is 1 .55％.关键词
锂电池/荷电状态/健康状态/自适应扩展卡尔曼滤波器Key words
lithium battery/state of charge (SOC)/state of health (SOH)/adaptive extended Kalman filter (AEKF)分类
信息技术与安全科学引用本文复制引用
Daniil Fadeev,张小周,董海鹰,刘浩,张蕊萍..基于模糊卡尔曼滤波器的锂电池荷电状态与健康状态预测[J].测试科学与仪器,2020,11(1):63-69,7.基金项目
Open Fund Project of State Key Laboratory of Large Electric Transmission Systems and Equipment Technology (No.SKLLDJ042017005) （No.SKLLDJ042017005）
