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| Remaining Useful Life Forecast of Li-Ion Batteries under Randomized Use |
| Received:August 04, 2018 Revised:December 25, 2018 |
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| DOI：10.7643/ issn.1672-9242.2018.12.004 |
| KeyWord:li-ion battery gaussian process regression life forecast randomized use |
| Author | Institution |
| LIU Jian |
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University, Shanghai , China |
| CHEN Zi-qiang |
Collaborative Innovation Center for Advanced Ship and Deep-Sea Exploration, State Key Laboratory of Ocean Engineering of Shanghai Jiao Tong University, Shanghai , China |
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| Abstract: |
| Objective To simulate the actual use of battery in ocean engineering and underwater scientific research equipment, carry out the li-ion battery aging test with random discharging current and predict remaining useful life (RUL) through Gaussian process regression (GPR) model. Methods GPR model with uncertainty expression ability was proposed based on data-driven methods. After selecting the kernel function, the forecast model was established by training data to optimize hyper-parameters. The data set of charge/discharge tests of li-ion battery under randomized use was used to verify the prognosis results. Results Compared with SE kernel function, the GPR model with Matern kernel function could get better prediction results. The more the training data was and the larger the starting prediction point was, the smaller the absolute error was, the lower MAPE and the RMSE values were. As for three sets of batteries under two different temperature and two kinds of random discharging modes, the GPR model can get accurate prediction results. The absolute error was no more than 40cycle and the MAPE and RMSE values were lower than 0.06 and 0.09 respectively. Conclusion The GPR model has high accuracy and strong applicability for RUL forecast of li-ion battery under randomized use. |
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