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Model migration neural network for predicting battery aging trajectories
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Tang, Xiaopeng, Liu, Kailong, Wang, Xin, Gao, Furong, Marco, James and Widanage, Widanalage Dhammika (2020) Model migration neural network for predicting battery aging trajectories. IEEE Transactions on Transportation Electrification, 6 (2). pp. 363-374. doi:10.1109/TTE.2020.2979547 ISSN 2372-2088.
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WRAP-model-migration-neural-network-predicting-battery-trajectories-Widanage-2020.pdf - Accepted Version - Requires a PDF viewer. Download (3711Kb) | Preview |
Official URL: http://dx.doi.org/10.1109/TTE.2020.2979547
Abstract
Accurate prediction of batteries’ future degradation is a key solution to relief users’ anxiety on battery lifespan and electric vehicle’s driving range. Technical challenges arise from the highly nonlinear dynamics of battery aging. In this paper, a feed-forward migration neural network is proposed to predict the batteries’ aging trajectories. Specifically, a base model that describes the capacity decay over time is first established from the existed battery aging dataset. This base model is then transformed by an input-output slope-and-bias-correction (SBC) method structure to capture the degradation of target cell. To enhance the model’s nonlinear transfer capability, the SBC-model is further integrated into a four-layer neural network, and easily trained via the gradient correlation algorithm. The proposed migration neural network is experimentally verified with four different commercial batteries. The predicted RMSEs are all lower than 2.5% when using only the first 30% of aging trajectories for neural network training. In addition, illustrative results demonstrate that a small size feed-forward neural network (down to 1-5-5-1) is sufficient for battery aging trajectory prediction.
Item Type: | Journal Article | |||||||||||||||
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Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering T Technology > TL Motor vehicles. Aeronautics. Astronautics |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | |||||||||||||||
Library of Congress Subject Headings (LCSH): | Electric vehicles, Electric vehicles -- Batteries, Electric vehicles -- Batteries -- Testing, Lithium ion batteries, Lithium ion batteries -- Deterioration | |||||||||||||||
Journal or Publication Title: | IEEE Transactions on Transportation Electrification | |||||||||||||||
Publisher: | IEEE | |||||||||||||||
ISSN: | 2372-2088 | |||||||||||||||
Official Date: | June 2020 | |||||||||||||||
Dates: |
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Volume: | 6 | |||||||||||||||
Number: | 2 | |||||||||||||||
Page Range: | pp. 363-374 | |||||||||||||||
DOI: | 10.1109/TTE.2020.2979547 | |||||||||||||||
Status: | Peer Reviewed | |||||||||||||||
Publication Status: | Published | |||||||||||||||
Reuse Statement (publisher, data, author rights): | © 2020 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 | |||||||||||||||
Date of first compliant deposit: | 17 March 2020 | |||||||||||||||
Date of first compliant Open Access: | 23 March 2020 | |||||||||||||||
RIOXX Funder/Project Grant: |
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