Early battery lifetime prediction based on statistical health features and box-cox transformation

Qiqi Wang, Min Xie, Fangfang Yang*

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

5 Citations (Scopus)

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.

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Original languageEnglish
Article number112594
JournalJournal of Energy Storage
Volume96
Online published25 Jun 2024
DOIs
Publication statusPublished - 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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