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 | Conference: | IEEE Conference on Industrial Electronics and Applications (7th : 2012 : Singapore) | 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 | Schools: | School of Electrical and Electronic Engineering | Fulltext Permission: | none | Fulltext Availability: | No Fulltext |
Appears in Collections: | EEE Conference Papers |
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