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

作　　者：高哲[1,2] 柴浩宇 焦芷媛 宋丹丹[1] GAO Zhe;CHAI Hao-yu;JIAO Zhi-yuan;SONG Dan-dan(School of Mathematics and Statistics,Liaoning University,Shenyang 110036,China;College of Light Industry,Liaoning University,Shenyang 110036,China)

机构地区：[1]辽宁大学数学与统计学院,辽宁沈阳110036 [2]辽宁大学轻型产业学院,辽宁沈阳110036

出　　处：《辽宁大学学报（自然科学版）》2024年第2期158-168,共11页Journal of Liaoning University：Natural Sciences Edition

基　　金：沈阳市中青年科技创新人才支持计划项目(RC210082);辽宁省教育厅科研基金项目(LJC202010);辽宁省自然科学基金项目(20180520009)。

摘　　要：锂离子电池因其能量密度高、使用寿命长等优点被越来越多地应用到卫星、便携式设备和电动汽车等领域.荷电状态作为电池管理系统的重要指标,它的准确监测对保障电池的使用安全、提高电池的使用效率有着重要意义.针对锂离子电池的荷电状态估计,本文提出了一种自适应中心差分卡尔曼滤波算法.首先,设计了一个线性卡尔曼滤波器实现了对测量方程系数的实时估计,从而避免了荷电状态与开路电压关系曲线的测试.其次,考虑到部分工况难以准确地获取模型参数,使用增广向量法并采用自适应中心差分卡尔曼滤波器实现了荷电状态与模型参数的自适应估计.再次,将线性卡尔曼滤波器与自适应中心差分卡尔曼滤波器耦合,实现了荷电状态、模型参数、测量方程系数的联合估计,使得本文所提算法能够更好地应用于电池内部参数未知的复杂工况.为了进一步提高算法的估计精度和对噪声的适应能力,通过迭代法对噪声协方差矩阵进行了动态调整.最后,通过几组实验验证了本文所提算法的有效性.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 

分 类 号：TM912[电气工程—电力电子与电力传动]