自动引导车电池SOC估算方法研究

Research on SOC Estimation Algorithm Applied to AGV vehicle

摘要:

自动引导车(AGV车)工况特殊,电流积分法估算电池剩余容量(SOC)误差较大,而且存在累积误差;为了提高AGV车电池剩余容量估算的准确度,对扩展卡尔曼滤波法估算AGV车电池剩余容量进行了研究,分析了AGV车特殊工况,提出将扩展卡尔曼滤波法的滤波增益改进为动态调整滤波增益,有效提高扩展卡尔曼滤波法的跟踪效果;实验表明使用扩展卡尔曼滤波法估算AGV车电池剩余容量精度较高,采用动态校正的滤波增益提高了估算过程的跟踪效果,解决了AGV车电池剩余容量估算不准确的问题。

Abstract:

The working condition of autonomous guided vehicle (AGV) is special, The error of battery SOC is estimated by the current integration method, and also there is a cumulative error. The SOC estimation accuracy can be improved by using the EKF method. Aiming at the special working conditions of the AGV vehicle, the filtering gain of the EKF method is improved for the dynamic adjustment of the filter gain. Effectively improve the tracking performance of the EKF method. The experimental results show that using the EKF method to estimate the SOC of the AGV car battery is higher, The tracking effect of the estimation process is improved by using the filter gain of the dynamic correction, which solves the problem of inaccurate estimation of the SOC of the battery in the special condition of the AGV vehicle.