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  • 标题:Online Estimation of Li-ion Battery SOC for Electric Vehicles Based on An Improved AEKF
  • 本地全文:下载
  • 作者:Kaihui Feng ; Bibin Huang ; Qionghui Li
  • 期刊名称:E3S Web of Conferences
  • 印刷版ISSN:2267-1242
  • 电子版ISSN:2267-1242
  • 出版年度:2019
  • 卷号:118
  • 页码:1-4
  • DOI:10.1051/e3sconf/201911802025
  • 语种:English
  • 出版社:EDP Sciences
  • 摘要:The purpose of this paper is to discuss how to eliminate the influence of noise time -varying characteristics on the accuracy of SOC estimation. Based on the matlab/simulink platform, the Thevenin equivalent circuit model of the battery is built, and an improved Adaptive Extend Kalman Filter (AEKF) is designed, which is compared with the Extend Kalman filter algorithm (EKF).The simulation results are shown that the improved AEKF algorithm results in effective online estimation SOC and the estimation accuracy is higher than the EKF algorithm.
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