Remaining Useful Life Prediction of Lithium-ion Battery Based on Discrete Wavelet Transform

Yujie WANG, Rui PAN, Duo YANG, Xiaopeng TANG, Zonghai CHEN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

40 Citations (Scopus)


The remaining useful life (RUL) and state-of-health (SoH) are critical to battery management system (BMS) to ensure the safety and reliability of electric vehicle (EV) operation. Recent literatures have reported various methods to estimate the RUL and SoH with a focus on the capacity loss, internal resistance increase, voltage drop, self-discharge, number of cycles, etc. However, most of the works only consider the battery in certain cases without thinking about the factors in real operation. In some cell internal parameters identification approaches, the accuracy of prediction results highly depends on the model and identification method. Aiming at these problems, a model free RUL prediction method is proposed in this work based on the discrete wavelet transform (DWT). The dynamic stress test (DST) schedule is conducted on the commercial 1665130-type lithium-ion battery and various outputs with non-stationary and transient phenomena are obtained and analyzed. Finally, experiments are conducted on the lithium-ion battery with different aging levels to verify the proposed method, and the results indicate that high accuracy of RUL prediction can be obtained by this quantitative correlation.

Original languageEnglish
Pages (from-to)2053-2058
Number of pages6
JournalEnergy Procedia
Publication statusPublished - May 2017
Externally publishedYes
Event8th International Conference on Applied Energy, ICAE 2016 - Beijing, China
Duration: 8 Oct 201611 Oct 2016

Bibliographical note

Publisher Copyright:
© 2017 The Authors.


  • Battery management
  • Discrete wavelet transform (DWT)
  • Remaining useful life (RUL)
  • State-of-health (SoH)


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