电动汽车SOC估算方法

SOC Estimation Algorithm of Electric Vehicle

由于电动汽车的电池组受到温度、充放电次数及电池老化等方面因素的影响,导致现有的剩余电量预测技术很难得到精确和可靠的结果。文章简要介绍现有的电池剩余电量估计的方法:安时计量法,电压测量法,内阻法,神经网络和模糊推理的方法及卡尔曼滤波法,并对这几种方法进行了实用性分析。表明卡尔曼滤波法是目前比较有价值的研究方向,且应用前景广泛。

Because of such factors influencing on battery file as temperature, time of charge and discharge and aging of the battery, it is difficult to get the accurate and reliable result with the existing estimation technology of the rest charge. The paper introduces several traditional methods of SOC estimation： ampere hour measurement, voltage test , internal resistance, neural network, fuzzy control measurement and kalman filter method. And the paper makes practical analysis on those above-mentioned ones, which indicates that the kalman filter method, with a broad application prospect, represents the current valuable research direction.