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|dc.description.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.||en|
|dc.title||Li-ion battery SOC estimation using EKF based on a model proposed by extreme learning machine||en|
|dc.contributor.school||School of Electrical and Electronic Engineering||en|
|dc.contributor.conference||IEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore)||en|
|Appears in Collections:||EEE Conference Papers|
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