Lifespan prediction of lithium-ion batteries based on various extracted features and gradient boosting regression tree model

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

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Original languageEnglish
Article number228654
Journal / PublicationJournal of Power Sources
Online published17 Aug 2020
Publication statusPublished - 15 Nov 2020


Accurate battery lifespan prediction is critical for the quality evaluation and long-term planning of battery management systems. As battery degradation process is typically nonlinear, accurate early prediction of cycle life with significantly less degradation is extremely challenging. Approaches using machine learning techniques, which are mechanism-agnostic alternatives, to predict battery lifespan are therefore becoming more and more attractive. In this paper, a gradient boosting regression tree (GBRT) is proposed to model complex nonlinear battery dynamics and predict battery lifespan through various extracted battery features. Essentially, the GBRT works by constructing additional trees through minimizing the prediction residues from existing base models. It can identify complex and nonlinear end-to-end relationship and meanwhile provide relative importance for each input feature. Various potential features including voltage-related features, capacity-related features, and temperature-related features are constructed in both discharge time dimension and life cycle dimension and explored for effective lifespan prediction. Key hyper-parameters including learning rate, number of trees, and maximum number of splits are investigated for optimal GBRT prediction. Comparative studies confirm that the proposed method is significantly superior to other machine learning algorithms for battery lifespan prediction with limited data samples and various input features, with mean average percentage error around 7%.

Research Area(s)

  • Feature extraction, Gradient boosting regression tree, Lifespan prediction, Lithium-ion batteries, Machine learning