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Future ageing trajectory prediction for lithium-ion battery considering the knee point effect
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Liu, Kailong, Tang, Xiaopeng, Teodorescu, Remus, Gao, Furong and Meng, Jinhao (2022) Future ageing trajectory prediction for lithium-ion battery considering the knee point effect. IEEE Transactions on Energy Conversion, 37 (2). pp. 1282-1291. doi:10.1109/TEC.2021.3130600 ISSN 0885-8969.
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Official URL: http://dx.doi.org/10.1109/TEC.2021.3130600
Abstract
Lithium-ion batteries have been widely applied in energy conversion sectors, where effective future ageing prediction is crucial to guarantee their safety and performance. Due to the highly nonlinear ageing behaviours, developing a reliable method that could not only consider the knee point effect but also predict the future ageing trajectory with uncertainty quantification poses a formidable task. This paper derives a machine learning solution, based on the migrated Gaussian process regression (GPR), for predicting future battery two-stage ageing trajectory. Specifically, a base model is first offline identified from the easier collected accelerated-speed ageing data, through which the long life ageing information can be effectively learned. With this base model, a migrated mean function is then designed and coupled within the GPR framework for battery ageing predictions. Experimental data from three different batteries are applied for model validation and performance evaluation. Results indicate that the proposed solution leads to effective improvements in prediction accuracy and uncertainty quantification for both cases of training before and after the knee point. This is the first time to couple migration concept within GPR, paving the way to reduce experimental cost and predict battery future two-stage ageing trajectory with only a few (first 30%) data available.
Item Type: | Journal Article | |||||||||||||||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | |||||||||||||||||||||
Journal or Publication Title: | IEEE Transactions on Energy Conversion | |||||||||||||||||||||
Publisher: | IEEE | |||||||||||||||||||||
ISSN: | 0885-8969 | |||||||||||||||||||||
Official Date: | June 2022 | |||||||||||||||||||||
Dates: |
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Volume: | 37 | |||||||||||||||||||||
Number: | 2 | |||||||||||||||||||||
Page Range: | pp. 1282-1291 | |||||||||||||||||||||
DOI: | 10.1109/TEC.2021.3130600 | |||||||||||||||||||||
Status: | Peer Reviewed | |||||||||||||||||||||
Publication Status: | Published | |||||||||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||||||||||
Access rights to Published version: | Restricted or Subscription Access | |||||||||||||||||||||
Copyright Holders: | IEEE | |||||||||||||||||||||
RIOXX Funder/Project Grant: |
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