Li-ion battery SOC estimation using EKF based on a model proposed by extreme learning machine
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, cou...
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| Main Authors: | , , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2013
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| 在線閱讀: | https://hdl.handle.net/10356/98879 http://hdl.handle.net/10220/12835 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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. |
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