State of Health Estimation for Lithium-ion Battery Using Fuzzy Entropy and Support Vector Machine

2020 ◽  
Author(s):  
Xin Sui ◽  
Shan He ◽  
Daniel-Ioan Stroe ◽  
Remus Teodorescu
Keyword(s):  
Support Vector Machine ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
Fuzzy Entropy ◽  
Support Vector ◽  
State Of Health

Lithium-ion battery state of health estimation with short-term current pulse test and support vector machine

2018 ◽  
Vol 88-90 ◽  
pp. 1216-1220 ◽  
Author(s):  
Jinhao Meng ◽  
Lei Cai ◽  
Guangzhao Luo ◽  
Daniel-Ioan Stroe ◽  
Remus Teodorescu
Keyword(s):  
Support Vector Machine ◽  
Lithium Ion Battery ◽  
Current Pulse ◽  
Lithium Ion ◽  
Support Vector ◽  
State Of Health ◽  
Short Term ◽  
Pulse Test

scholarly journals A Novel State of Charge Approach of Lithium Ion Battery Using Least Squares Support Vector Machine

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 195398-195410
Author(s):  
Jiabo Li ◽  
Min Ye ◽  
Wei Meng ◽  
Xinxin Xu ◽  
Shengjie Jiao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Lithium Ion Battery ◽  
Lithium Ion ◽  
State Of Charge ◽  
Support Vector

RUL Prediction of Lithium Ion Battery Based on ARIMA Time Series Algorithm

2020 ◽  
Vol 999 ◽  
pp. 117-128
Author(s):  
Cun Lu ◽  
Zheng Jian Gu ◽  
Yuan Yan
Keyword(s):  
Time Series ◽  
Support Vector Machine ◽  
Energy Storage ◽  
Lithium Ion Battery ◽  
Time Series Prediction ◽  
Storage System ◽  
Lithium Ion ◽  
Energy Storage System ◽  
Remaining Useful Life ◽  
Support Vector

Lithium ion battery is a key component of energy storage system. Accurate and scientific prediction of its Remaining Useful Life (RUL) is an important factor to check the operation of energy storage system is whether reliable. ARIMA is an effective time series prediction processing method, which can be used to calculate battery RUL and its confidence interval. And the more predicted samples, the higher the prediction accuracy. Compared with the empirical model and support vector machine algorithm, the analysis results show that the support vector machine is over-fitting. For two sets of the experimental data, the absolute predictive error of ARIMA algorithm is approximately 1.2%, that of linear model is approximately 1.4%, and that of Verhulst model is approximately 7.5%, which verifies the accuracy of ARIMA time series model in predicting the RUL in long interval.

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