RS-SVM在电动汽车电池荷电状态预估上的应用研究  

Study on the application of RS-SVM to the estimation of battery charge state of electric vehicles

作　　者：牛国成[1] 胡冬梅[1] 白晶[1] 

机构地区：[1]北华大学电气信息工程学院,吉林吉林132021

出　　处：《内蒙古科技大学学报》2015年第4期360-363,共4页Journal of Inner Mongolia University of Science and Technology

基　　金：国家科技型中小型企业技术创新资助项目(12C26212201322)

摘　　要：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,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.The method based on RS-SVM was proposed to improve the charging,discharging and maintenance of power battery for electric vehicles. Support vector machine was adopted to predict the charge state of the battery by using the rough set to reduce the samples of the battery-charging related parameters. This method not only improved the prediction accuracy and the speed of support vector machine,but also increased the service life of the battery.

关 键 词：简约粗糙集 支持向量机 荷电状态 

分 类 号：TE08[石油与天然气工程] F206[经济管理—国民经济]