电工技术学报2017，Vol.32Issue(21)：1-8,8.DOI:10.19595/j.cnki.1000-6753.tces.160837
基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算
Estimation of Lithium-Ion Battery State of Charge Based on Grey Prediction Model-Extended Kalman Filter
摘要
Abstract
Accurate estimation of battery state of charge (SOC) is one of the core technologies of the battery management system.In order to improve accuracy of battery SOC estimation of extended Kalman filter,the grey prediction model (GM) and extended Kalman filter (EKF) are fused to build the GM-EKF algorithm and estimate battery SOC.Firstly,The GM (1,1) is used to replace the Jacobian matrix in EKF and predict the current battery system status as a priori estimate.Then,the priori estimate is updated and revised through observations to get posterior estimation and obtain the battery SOC.The simulated working condition test of battery is implemented on the self-build experiment platform.The experimental results show that GM-EKF algorithm has higher estimation accuracy comparing to EKF algorithm on battery SOC estimation,and the estimation error is less than ± 0.005.The research result has realistic guiding meanings on battery SOC estimation for battery management system.关键词
锂离子电池/电池荷电状态/灰色预测模型/扩展卡尔曼滤波Key words
Lithium-ion battery/state of charge/grey prediction model/extended Kalman filter分类
交通工程引用本文复制引用
潘海鸿,吕治强,李君子,陈琳..基于灰色扩展卡尔曼滤波的锂离子电池荷电状态估算[J].电工技术学报,2017,32(21):1-8,8.基金项目
国家自然科学基金(51267002,51667006)、广西自然科学基金(2015GXNSFAA139287)、广西制造系统与先进制造技术重点实验室项目(15-140-30S002)和广西研究生教育创新计划项目(YCSW2017038)资助. （51267002,51667006）
