State of charge estimation of lithium battery based on Dual Adaptive Unscented Kalman Filter

Peng Zhang, C. Xie, Shibao Dong
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引用次数: 1

Abstract

Accurate battery state of charge (SOC) estimation is one of most significant functions in the battery management system (BMS). Estimating the SOC of lithium battery based on the traditional unscented Kalman filter (UKF) algorithm has better estimation precision, but the application condition of the algorithm is that the statistical properties of the state process noises and measurement noises are known clearly. This paper proposed the dual adaptive unscented Kalman filter (DAUKF) algorithm which is combining the traditional Kalman filter (KF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm. Firstly, a second-order Resistor-Capacitor (RC) model of lithium battery is established, the KF algorithm is used to identify the model parameters online. Secondly, the AUKF algorithm based on Unscented Transformation (UT) is utilized to estimate the SOC. And then the implementation steps of the DAUKF algorithm are introduced in detail. The experimental results indicate that the DAUKF can estimate the SOC with error less than 1.0% under the conditions of the constant exile electric, the constant current charge and discharge and the urban dynamometer driving schedule (UDDS) cycle. Compared with the dual unscented Kalman filter (DUKF), the DAUKF has stronger estimation precision and better adaptive tracking ability.
基于双自适应无气味卡尔曼滤波的锂电池充电状态估计
准确估计电池荷电状态(SOC)是电池管理系统(BMS)中最重要的功能之一。基于传统的无气味卡尔曼滤波(UKF)算法估计锂电池荷电状态具有较好的估计精度,但该算法的应用条件是状态过程噪声和测量噪声的统计特性是已知的。本文提出了将传统卡尔曼滤波(KF)算法与自适应无气味卡尔曼滤波(AUKF)算法相结合的双自适应无气味卡尔曼滤波(DAUKF)算法。首先,建立了锂电池的二阶电阻-电容(RC)模型,利用KF算法在线辨识模型参数;其次,利用基于Unscented变换(UT)的AUKF算法对SOC进行估计;然后详细介绍了DAUKF算法的实现步骤。实验结果表明,在恒流放电、恒流充放电和城市测功仪行驶计划(UDDS)循环条件下,DAUKF估计SOC的误差小于1.0%。与双无气味卡尔曼滤波(DUKF)相比,该算法具有更强的估计精度和更好的自适应跟踪能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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