Lithium-ion Batteries Capacity Degradation Trajectory Prediction Based on Decomposition Techniques and NARX Algorithm

Ma'd El-Dalahmeh, IMRAN Bashir, Maher Al-Greer, Mo'Ath El-Dalahmeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution


It is critical to accurately predict the remaining capacity of lithium-ion batteries to guarantee safe, reliable operation with minimal maintenance costs. However, because of the complicated and nonlinear characteristics of the battery’s degradation throughout its lifetime, predicting the amount of capacity that will still be available in lithium-ion batteries is a complex process. In addition, the phenomena of capacity regeneration have a significant impact on the accuracy of the remaining capacity projection. For this purpose, the signal decomposition method is becoming a more attractive and promising method for overcoming the difficulty of the capacity regeneration phenomena due to its simplicity and capability to accommodate the nonlinear dynamic behaviour of the lithium-ion battery. Therefore, this paper investigates the performance of three signal decomposition techniques: the discrete wavelet transforms, the empirical mode decomposition, and the variational mode decomposition techniques in analysing the capacity regeneration phenomenon. The nonlinear autoregressive neural network algorithm is developed to predict the trajectory of the future capacity of the battery. The performance of the proposed algorithms is analysed by using two datasets from NASA Ames Research centre and the centre for advanced life cycle engineering (CALCE). The comparison results demonstrate that the variational mode decomposition method combined with the nonlinear autoregressive neural network outperforms other methods with 2.385% RMSE and 1.6% MAE.
Original languageEnglish
Title of host publication57th International Universites Power Engineering Conference (UPCK 2022)
Subtitle of host publicationBig Data and Smart Grids, UPEC 2022 - Proceedings
Number of pages6
ISBN (Electronic)9781665455053
Publication statusPublished - 18 Oct 2022
Event57th International Universities Power Engineering Conference (UPEC) - Istanbul, Turkey
Duration: 30 Aug 20222 Sept 2022

Publication series

Name2022 57th International Universities Power Engineering Conference: Big Data and Smart Grids, UPEC 2022 - Proceedings


Conference57th International Universities Power Engineering Conference (UPEC)
Internet address

Bibliographical note

Publisher Copyright:
© 2022 IEEE.


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