Early-stage lifetime prediction for lithium-ion batteries : A deep learning framework jointly considering machine-learned and handcrafted data features

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

13 Scopus Citations
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Detail(s)

Original languageEnglish
Article number104936
Journal / PublicationJournal of Energy Storage
Volume52
Issue numberB
Online published2 Jun 2022
Publication statusPublished - 15 Aug 2022

Abstract

Predicting the battery lifetime at its early stage is a promising technology for accelerating the battery development, production, and design optimization. However, it is a challenging task for most existing prediction methods because information is too limited in early life cycles, and the early-cycle capacity data exhibits a weak correlation with the target battery lifetime. In this paper, to realize an accurate battery lifetime prediction via data obtained from just first few life cycles, we propose a three-stage deep learning framework. First, we develop an emerging two-channel data feature engineering process, which jointly consider a convolutional neural network based latent feature extraction and domain knowledge based handcrafted features. Next, a wrapper feature selection method is adopted to further compress the dimension of developed features via eliminating linearly correlated ones. Finally, processed data features are fed into a data-driven model to realize the early-stage battery lifetime prediction. Results of computational experiments show that our proposed joint consideration of machine-learned features and handcrafted features can improve early-stage battery lifetime predictions via comparing with state-of-the-art benchmarks. We also show that the proposed framework can be generalized to batteries cycled under different operation conditions.

Research Area(s)

  • Convolutional neural networks, Data-driven models, Early stage, Health feature extraction, Lifetime prediction, Lithium-ion battery

Citation Format(s)

Early-stage lifetime prediction for lithium-ion batteries: A deep learning framework jointly considering machine-learned and handcrafted data features. / Fei, Zicheng; Zhang, Zijun; Yang, Fangfang et al.
In: Journal of Energy Storage, Vol. 52, No. B, 104936, 15.08.2022.

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