基于AUKF的锂离子电池健康状态估计

摘要

To solve state of health (SOH) estimation problem of the lithium-ion battery,an adaptive unscented Kalman filtering algorithm (AUKF) is proposed,which can achieve SOH estimation through the covariance adaptive matching equation to suppress the noise interference.The state space model of lithium-ion battery is established and the ohm resistance is real-time estimated with AUKF to obtain the SOH using the relationship between the ohm resistance and SOH.The experimental results show that the proposed method is accurate and reliable,which provides an effective method for state estimation for battery management system.%针对锂离子动力电池健康状态(SOH)估计问题,提出一种自适应无迹卡尔曼滤波算法(AUKF),通过协方差自适应匹配方法抑制噪声干扰,实现SOH的准确估计.建立了锂离子动力电池的状态空间模型,采用AUKF实时估计电池内阻,利用电池欧姆内阻和SOH之间的内在关系,进而得到电池的SOH.实验结果表明,利用所提方法估计SOH准确、可靠,为电池管理系统中状态估计提供了一种有效的方法.