基于等效模型和多时间尺度扩展卡尔曼滤波的锂离子电池SOC预测

SOC Prediction of Lithium-ion Batteries Based on Equivalent Circuit model and Multi-time Scale Extended Kalman Filter

摘要:

荷电状态(SOC)和最大可用电量估计是锂离子电池寿命预测中的两个最重要部分；然而与快速时变的SOC比较,最大可用电量的参数变化缓慢；文章提出了一个基于等效模型和多时间尺度的扩展卡尔曼滤波(EKF)预测算法对SOC和最大可用容量分别在不同时间尺度上进行估计,在宏观尺度上利用了SOC估计值作为观测量,更新最大可用电量；针对NCA/C卫星锂离子电池实验数据的仿真结果表明,提出的多时间尺度EKF预测算法与EKF联合估计算法相比,SOC和最大可用电量估计准确度更高,同时提高了计算效率。

Abstract:

The state of charge (SOC) and the maximum available electricity estimation are two of the most important parts of satellite lithium-ion battery life prediction. However, compared with the fast time-varying SOC, the parameters of the maximum available power change slowly. It proposed a multi-time scale extended Kalman filter (EKF) prediction algorithm based-on the equivalent circuit model to estimate the SOC and the maximum available capacity at different time scales. The SOC estimation is used as an observation on the macroscopic scale to update the maximum available power. The simulation results of NCA/C lithium-ion battery show that the proposed multi-time scale EKF prediction algorithm has higher accuracy of SOC and maximum available power estimation compared with tradtional EKF algorithm, and improves the computational efficiency.