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Electrochemical-theory-guided modelling of the conditional Generative Adversarial Network for battery calendar ageing forecast
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Hu, Tianyu, Ma, Huimin, Sun, Hongbin and Liu, Kailong (2023) Electrochemical-theory-guided modelling of the conditional Generative Adversarial Network for battery calendar ageing forecast. IEEE Journal of Emerging and Selected Topics in Power Electronics, 11 (1). pp. 67-77. doi:10.1109/JESTPE.2022.3154785 ISSN 2168-6777.
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Official URL: http://dx.doi.org/10.1109/JESTPE.2022.3154785
Abstract
In many energy storage applications, the inevitable calendar ageing of batteries results in both shorted service life and decreased battery performance. In this paper, a Generative Adversarial Network-based (GAN-based) model is proposed for both point and probabilistic forecasts of battery calendar ageing, i.e., the Capacity Forecast GAN (CFGAN), which will be the first work that applies GAN to calendar ageing forecast. GAN’s ability to learn arbitrarily complex distributions has enabled CFGAN to approximate all the possible (arbitrarily shaped) joint distributions. By taking electrochemical knowledge as the guidelines for designing CFGAN’s crucial part, i.e., the conditioner, CFGAN has maintained a satisfying consistency between knowledge and data, making it both knowledge-driven and data-driven, i.e., knowledge+data-driven, which has improved its theoretic strength and forecast performance significantly. Illustrative results on practical calendar ageing case studies demonstrated the superiority of CFGAN in forecasting and generalizing to unwitnessed conditions, implying that the CFGAN built in deep structure has grasped the complex multi-modality of the condition-varying calendar ageing process.
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 Journal of Emerging and Selected Topics in Power Electronics | ||||||||||||||||||
Publisher: | IEEE | ||||||||||||||||||
ISSN: | 2168-6777 | ||||||||||||||||||
Official Date: | February 2023 | ||||||||||||||||||
Dates: |
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Volume: | 11 | ||||||||||||||||||
Number: | 1 | ||||||||||||||||||
Number of Pages: | 10 | ||||||||||||||||||
Page Range: | pp. 67-77 | ||||||||||||||||||
DOI: | 10.1109/JESTPE.2022.3154785 | ||||||||||||||||||
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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