辽宁大学学报（自然科学版）2024，Vol.51Issue(2)：158-168,11.
采用自适应中心差分卡尔曼滤波器的锂离子电池荷电状态估计
State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Center Differential Kalman Filter
摘要
Abstract
Lithium-ion batteries are increasingly being used in fields such as satellites,portable devices and electric vehicles due to their high energy density and long service life.As an important index of the battery management system,accurate monitoring of the state of charge is important for ensuring the safety of battery use and improving battery efficiency.An adaptive center differential Kalman filtering algorithm is proposed for estimating the state of charge of lithium-ion batteries.Firstly,this paper designs a linear Kalman filter to achieve the real-time estimations of the coefficients in the measurement equation,which avoids the testing of the relationship curve between the state of charge and the open circuit voltage.Secondly,considering that it is difficult to accurately obtain model parameters under certain operating conditions,the augmented vector method and adaptive central differential Kalman filter are used to achieve adaptive estimations of the state of charge and model parameters.Then,the linear Kalman filter and the adaptive center differential Kalman filter are coupled to achieve joint estimations of the state of charge,model parameters,and coefficients in the measurement equation,making the proposed algorithm better applicable to complex working conditions with unknown parameters inside the battery.In order to further improve the estimation accuracy and adaptability of the proposed algorithm to noises,the noise covariance matrices are dynamically adjusted via the iterative method.Finally,the effectiveness of the proposed algorithm is verified by several sets of experiments.关键词
锂离子电池/荷电状态/中心差分卡尔曼滤波器/自适应估计Key words
lithium-ion battery/state of charge/center differential Kalman filter/adaptive estimation分类
信息技术与安全科学引用本文复制引用
高哲,柴浩宇,焦芷媛,宋丹丹..采用自适应中心差分卡尔曼滤波器的锂离子电池荷电状态估计[J].辽宁大学学报（自然科学版）,2024,51(2):158-168,11.基金项目
沈阳市中青年科技创新人才支持计划项目(RC210082) （RC210082）
辽宁省教育厅科研基金项目(LJC202010) （LJC202010）
辽宁省自然科学基金项目(20180520009) （20180520009）
