标题：Battery State-Of-Charge Estimation in Electric Vehicle Using Elman Neural Network Method
作者：Shi Qingsheng; Zhang Chenghui; Cui Naxin; Zhang Xiaoping
作者机构：[Shi Qingsheng; Zhang Xiaoping] Henan Univ Technol, Coll Elect Engn, Zhengzhou 450007, Peoples R China.; [Shi Qingsheng; Zhang Chenghui; Cui Naxin] 更多
会议名称：29th Chinese Control Conference
会议日期：JUL 29-31, 2010
来源：PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE
关键词：ElectricVehicle; ElmanNeural Network; State-Of-Charge; BPNeural Network
摘要：To accurately estimate the battery State-Of-Charge (SOC) during the Electric Vehicle driving process is an important problem, which urgently awaits to be solved. Elman neural network, which has good dynamic property, quick approximate rate and high prediction accuracy, is adopted to estimate battery SOC. First, the training data and the test data required in the estimation operation are collected using the ADVISOR software, which include the attributes, such as the battery current, voltage, required power and SOC. Then, to avoid attributes in greater numeric ranges dominate those in smaller numeric ranges and the numerical difficulties during the calculation, data scaling should be operated before applying the Elman neural network. Finally, compared to the BP neural networkmethod, simulation experiments have been carried on. The results indicate that, under different drive cycles, using Elman neural network can more accurately approximate the actual SOC value and then obtain a better estimation performance.