中文摘要：

近几年,磷酸铁锂动力电池逐渐成为电动汽车动力电池首选。但是由于材料本身特性,使得磷酸铁锂电池的荷电状态难以精确估算。当电动汽车处于复杂工作环境时,荷电状态估计在保证电动汽车电池操作中的安全性和可靠性方面起到了至关重要的作用。文章采用戴维宁等效电路模型,验证无迹卡尔曼滤波和粒子滤波两种方法的估算效果,并分别与扩展卡尔曼滤波方法作对比,结果证明无迹卡尔曼滤波和粒子滤波都具有更好的估算精度。

英文摘要：

Abstract In recent years, lithium iron phosphate （LiFePO4） power battery is widely used for electric vehicle. However, it is difficult to estimate the state of charge（SOC） of battery because of the characteristics of material itself. In complicated operation environments, SOC estimation plays a significant role in ensuring safety and reliability of battery operations for an electric vehicle. In this paper, both unscented Kalman filter and Particle Filter methods of a LiFePO4 battery for applications in electric vehicles were verified using Thevenin equivalent circuit model. Compared with the extended Kalman filter method, results show that both unscented Kalman filter and particle filter have a better estimation accurancy.