Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/98879
Title: Li-ion battery SOC estimation using EKF based on a model proposed by extreme learning machine
Authors: Du, Jiani
Liu, Zhitao
Chen, Can
Wang, Youyi
Issue Date: 2012
Abstract: In this paper, a method for modeling and estimation of Li-ion battery state of charge (SOC) using extreme learning machine (ELM) and extended Kalman filter (EKF) is proposed. The Li-ion battery model from ELM, which is established by training the data from the battery block in MATLAB/Simulation, could describe the dynamics of Li-ion battery very well. And it has higher accuracy and needs less calculation than using the traditional neural networks. Moreover, the battery model and discrete SOC definition equation constitute state-space equations, and EKF is used to estimate the SOC of Li-ion battery. Comparing the actual SOC with the estimated SOC by simulation, it reveals that the method proposed in this paper has good performance on Li-ion battery SOC estimation.
URI: https://hdl.handle.net/10356/98879
http://hdl.handle.net/10220/12835
DOI: 10.1109/ICIEA.2012.6360990
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Conference Papers

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