基于卡尔曼滤波算法的电池状态估计
Battery State Estimation Based on Kalman Filter Algorithm
王语园 1安盼龙 1惠亮亮1
作者信息
- 1. 陕西铁路工程职业技术学院铁道动力学院,渭南 714000
- 折叠
摘要
为更好地获得锂离子电池荷电状态 SOC(state-of-charge)估计值,选用二阶等效电路模型作为研究对象,针对带有遗忘因子的递推最小二乘法在参数辨识中易受到噪声等环境因素干扰的缺点,提出偏差补偿最小二乘法来实现模型参数的准确辨识,并结合无迹卡尔曼滤波算法对SOC进行估计.针对无迹卡尔曼滤波算法稳定性差等缺点,提出利用权重向量更新滤波算法中的卡尔曼滤波增益.实验结果表明,所提算法估计SOC的总误差可控制在2.7%以内,验证了算法的鲁棒性和有效性.
Abstract
To obtain the state-of-charge(SOC)estimation value well,a second-order equivalent circuit model is selected as the research object.Aimed at the disadvantage that the recursive least squares method with a forgetting factor is easy to be disturbed by environmental factors such as noises in the parameter identification,a bias compensation recursive least squares method is proposed to realize the accurate identification of model parameters,and the SOC is estimated combined with the unscented Kalman filter algorithm.In view of the disadvantages of the unscented Kalman filter algorithm such as poor stability,the weight vectors are used to update the Kalman filter gain in the filter algorithm.Experimental results show that the total error of the proposed algorithm in estimating SOC was controlled within 2.7%,which verified the robustness and effectiveness of the algorithm.
关键词
电池管理系统/锂离子电池/荷电状态/偏差补偿最小二乘法/无迹卡尔曼滤波/权重向量Key words
Battery management system/lithium-ion battery/state-of-charge(SOC)/bias compensation recursive least squares method/unscented Kalman filter/weight vector引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据