State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network  

作　　者：毕军 邵赛 关伟 王璐 

机构地区：[1]School of Traffic and Transportation,Beijing Jiaotong University [2]MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology,Beijing Jiaotong University

出　　处：《Chinese Physics B》2012年第11期560-564,共5页中国物理B（英文版）

基　　金：Project supported by the National High Technology Research and Development Program of China (Grant No. 2011AA110303);the Beijing Municipal Science & Technology Project,China (Grant No. Z111100064311001)

摘　　要：The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.The on-line estimation of the state of charge （SOC） of the batteries is important for the reliable running of the pure electric vehicle in practice. Because a nonlinear feature exists in the batteries and the radial-basis-function neural network （RBF NN） has good characteristics to solve the nonlinear problem, a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed. Firstly, in this paper, the model of on-line SOC estimation with the RBF NN is set. Secondly, four important factors for estimating the SOC are confirmed based on the contribution analysis method, which simplifies the input variables of the RBF NN and enhttnces the real-time performance of estimation. FiItally, the pure electric buses with LiFePO4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object. The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.

关 键 词：state of charge estimation BATTERY electric vehicle radial-basis-function neural network 

分 类 号：TM912[电气工程—电力电子与电力传动]