RS-SVM在电动汽车电池荷电状态预估上的应用研究
牛国成1胡冬梅1*白晶1
（1北华大学电气信息工程学院吉林吉林 132021）
关键词：简约粗糙集；支持向量机；荷电状态
中图分类号：TE08，F206文献标识码：A
摘要为了提高电动汽车动力电池的充电，放电和维护的技术水平，提出了RS-SVM的电动汽车电池荷电状态的预估方法。采用粗糙集约简电池充电相关参数的样本，运用支持向量机对电池的荷电状态进行预测，该方法提升了支持向量机的预测速度和精度，达到增加电池使用寿命的目的。
Study on the application of RS-SVM in electricvehicle battery charge state
NIUGuo-cheng，HU Dong-mei，BAI Jing
(College of Electrical and InformationEngineering，Beihua University，Jilin 132021，China)
Keywords:Simplerough set; Support vectormachines (SVM);State of charge（SOC）
Abstract The method based on RS-SVM was proposedto improve the charging ,dischargingand maintenance of power battery for electric vehicles.Using the rough set to reducethe samples of thebattery-charging related parameters, support vector machine was adopted to predict the charge state ofthe battery. This method not only improved theprediction accuracy and the speed of support vector machine, but also increasedthe service life of the battery.