基于EKF与线性回归的锂离子电池状态估计
Estimation of Lithium-ion Batteries'State Based on EKF and Linear Regression
雷雨 1刘凡 1刘小丽 1宋瑞琦1
作者信息
- 1. 陆军工程大学通信士官学校,重庆 400035
- 折叠
摘要
锂离子电池因其高能量密度、长寿命、低自放电率等特性,在社会各领域被应用广泛,成为现代科技和社会发展的重要基石.但锂离子电池包含复杂的电化学系统,状态检测难度较大,不利于供电系统的稳定性与可靠性.文章结合EKF(扩展卡尔曼滤波)与线性回归方法,构建了一种携行锂离子电池的状态(含SOC与SOH)估计模型.实验验证表明,该模型能有效估计电池的SOC与SOH,其中SOC平均估计误差为 1.54%,最大SOH估计误差为 2.73%.
Abstract
Due to high energy density,long life,and low self-discharge rate,lithium-ion batteries are widely used in various fields of society and have become an important cornerstone of modern technology and social development.However,lithium-ion batteries contain complex electrochemical systems,making state detection difficult and harmful to the stability and reliability of the power supply system.The article combined EKF(Extended Kalman Filter)and linear regression to construct a state(including SOC and SOH)estimation model for some kind of portable lithium-ion batteries.Experimental verification showed that the model can effectively estimate SOC and SOH,with an average SOC estimation error of 1.54%and a maximum SOH estimation error of 2.73%.
关键词
EKF/线性回归/锂电子电池/状态估计Key words
EKF/linear regression/lithium-ion batteries/state estimation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据