基于等效模型扩展卡尔曼滤波锂电池SOC估算

SOC Estimation of Lithium Battery Based on Equivalent Model of Extended Kalman Filter

电池管理系统(BMS)的主要任务是对电池荷电状态(SOC)、续航里程和防止电池过充过放等进行实时诊断,其中电池荷电状态的快速精确的估计是BMS的核心技术。基于锂电池这一动态非线性系统,提出了一种更接近于真实的、改进的PNGV电池等效模型;基于改进的PNGV电池等效模型,对比了卡尔曼滤波算法(KF)和扩展卡尔曼滤波算法(EKF)诊断电池荷电状态的实验结果;分析了扩展卡尔曼滤波算法诊断的实验误差。研究表明:采用扩展卡尔曼滤波算法对电池荷电状态进行诊断得到的结果更加精确,其误差能够一直保持在5%以内。

The main task of the battery management system(BMS)is to diagnose the battery state of charge(SOC),endurance mileage and prevent overcharge and discharge of batteries in real time.The rapid and accurate estimation of SOC is the core technology of BMS.Based on the dynamic nonlinear system of lithium battery,a more realistic and improved equivalent model of PNGV battery was proposed.Based on the improved PNGV battery equivalent model,the experimental results of battery SOC diagnosis of Kalman filter(KF)and extended Kalman filter(EKF)were compared.The experimental error of EKF was analyzed.The results show that EKF is more accurate in diagnosing battery SOC,and the error can be kept within 5%.