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Abstract
Early battery lifetime prediction is important for both safety reasons and battery development. It predicts battery lifetime before it degrades significantly and has the advantage of being low-cost, time-saving, and providing timely feedback. However, due to the nonlinear degradation behavior and limited data in the early stage, early battery lifetime prediction is difficult. In this paper, statistical health features are extracted from temperature and voltage data, and a statistical transformation method is proposed to enhance the feature's importance and help increase the prediction accuracy of battery lifetime. First, candidate health features are constructed by analyzing temperature and voltage data, and Box-Cox transformation (BCT) is used to enhance their linear correlation with battery lifetime to help with feature selection. The Pearson correlation coefficients of extracted temperature and voltage features with battery lifetime reach −0.73 and − 0.90 respectively. Early battery lifetime prediction based on the first 100 cycles of data is then conducted using Gaussian process regression with a root-mean-square-error (RMSE) of 112.14 cycles. Comparison experiments show that Gaussian process regression outperforms the other four common machine learning methods in both BCT and non-BCT situations. Moreover, adding temperature feature and BCT have a positive effect. When they are not used, RMSEs would increase by 13.8 % and at most 209 %, respectively. At last, prediction based on fewer data (50 cycles) is tested and gained acceptable result of an RMSE of 149.68 cycles.
© 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
© 2024 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Original language | English |
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Article number | 112594 |
Journal | Journal of Energy Storage |
Volume | 96 |
Online published | 25 Jun 2024 |
DOIs | |
Publication status | Published - 15 Aug 2024 |
Funding
This work was supported by the National Natural Science Foundation of China ( 62203482 , 71971181 , 72032005 ), Guangdong Basic and Applied Basic Research Foundation ( 2021A1515110354 ), Research Grant Council of Hong Kong ( 11203519 , 11200621 ), and Hong Kong Innovation and Technology Commission ( InnoHK Project CIMDA ).
Research Keywords
- Lithium-ion battery
- Early lifetime prediction
- Box-Cox transformation
- Feature extraction
- Gaussian process regression
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