基于锂电池SOC估算方法  

SOC estimation algorithm base on lithium-ion battery

作　　者：刘莉[1] 胡社教[1] 

机构地区：[1]合肥工业大学计算机与信息学院,安徽合肥230009

出　　处：《电源技术》2017年第1期4-6,63,共4页Chinese Journal of Power Sources

摘　　要：电池的荷电状态(State of charge,SOC)是锂电池组电池管理系统的重要参数,而电池的SOC估算受到很多因素的综合影响,难以保证其估算精度。准确的电池模型是精确估算SOC的基础,通过对电池模型的改进、模型参数的实时更新,提高了模型参数的精确度;修正的扩展卡尔曼滤波并结合修正的安时积分法,减小了温度、充放电倍率等因素的影响,从而提高了SOC估算的精度。State of charge（SOC） was one of the important parameters of battery management system of lithium-ion battery, the SOC estimation was affected by many factors of comprehensive, so it was difficult to ensure the estimation accuracy. Accurate battery model was the basis of accurate estimation of SOC, through improvements on the battery model, real-time update of the model parameters, to improve the precision of the model parameters. Correction of the extended kalman filter and combined with modified Ah counting method, reduced the influence of factors such as temperature, charge and discharge rate, so as to improve the accuracy of the SOC estimation.

关 键 词：锂离子电池组 电池模型 扩展卡尔曼滤波 

分 类 号：TM912[电气工程—电力电子与电力传动]