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Prediction for state of health of lithium-ion batteries by local information fusion with ensemble support vector regression(PDF)


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Prediction for state of health of lithium-ion batteries by local information fusion with ensemble support vector regression
Chen Jianxin1Hou Jianming1Wang Xin1Shao Haitao1Song Guanglei1Xue Yu2
1.State Grid Xinjiang Information & Telecommunication Company,Urumqi 830000,China; 2.State Grid Electric Power Research Institute,NARI Group Corporation,Nanjing 210003,China
lithium-ion batteries state of health support vector regression ensemble learning information fusion
To improve the prediction accuracy of state of health(SOH)for lithium-ion batteries,this paper developes a local information fusion with ensemble support vector regression(LIF-ESVR)method,which is implemented by combining support vector regression(SVR)algorithm with ensemble learning theory. The basic idea of LIF-ESVR is to use local information fusion to replace the global information and switch the information fusion problem to the decision fusion problem. Firstly the original training dataset is divided into multiple subsets,each of which contains the important local information; then,for each subset,the corresponding SVR is trained on it; finally,the ensemble learning technology is adopted to incorporate multiple trained SVRs. The experimental results on batteries datasets of the National Aeronautics and Space Administration(NASA)of USA have demonstrated that the LIF-ESVR outperforms the existing methods for predicting lithium-ion batteries SOH and can be used practically and extensively.


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Last Update: 2018-02-28