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Transfer learning LSTM model for battery useful capacity fade prediction
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Gupta, Aniruddha, Sheikh, Muhammad, Tripathy, Yashraj and Widanage, W. Dhammika (2022) Transfer learning LSTM model for battery useful capacity fade prediction. In: 2021 24th International Conference on Mechatronics Technology (ICMT), Singapore, 18-22 Dec 2021. Published in: Proceedings of the 2021 24th International Conference on Mechatronics Technology (ICMT) pp. 1-6. ISBN 9781665424592. doi:10.1109/ICMT53429.2021.9687230
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Official URL: http://dx.doi.org/10.1109/ICMT53429.2021.9687230
Abstract
Lithiumion (Li-ion) batteries have become increasingly useful within the automotive industry and modern life applications due to high energy and power densities. However, these batteries suffer capacity loss due to different ageing mechanisms in various applications. Despite several existing models, lack of accurate predictability of capacity degradation limits the advancement of Li-ion batteries. The present work focuses on prediction of battery useful capacity degradation using long-short term memory (LSTM) transfer learning neural network model. At first, a base model was developed and trained using all the (100 % ) degradation data available at 0°C and 10°C environmental temperatures. Thereafter, the training of the base model was fixed, and additional hidden layers were added on top of the base model to further fine tune it with only the initial 30% degradation data available at 25°C environmental temperature. The remaining (70 % ) data of the 25°C case was tested for model prediction. To decide the number of fixed hidden layers to be transferred from base model to transfer model and the number of additional hidden layers on top, an optimization for minimum cross validation error was performed. It was found that the resulting model was able to forecast the remaining battery degradation with 96% accuracy. The model prediction was also compared with LSTM deep learning architecture without using transfer learning. The LSTM with transfer learning model was found to be 17% higher in prediction accuracy than that without utilizing transfer learning.
Item Type: | Conference Item (Paper) | ||||
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Journal or Publication Title: | Proceedings of the 2021 24th International Conference on Mechatronics Technology (ICMT) | ||||
Publisher: | IEEE | ||||
ISBN: | 9781665424592 | ||||
Book Title: | 2021 24th International Conference on Mechatronics Technology (ICMT) | ||||
Official Date: | 1 February 2022 | ||||
Dates: |
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Page Range: | pp. 1-6 | ||||
DOI: | 10.1109/ICMT53429.2021.9687230 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Reuse Statement (publisher, data, author rights): | © 2021 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 | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 2021 24th International Conference on Mechatronics Technology (ICMT) | ||||
Type of Event: | Conference | ||||
Location of Event: | Singapore | ||||
Date(s) of Event: | 18-22 Dec 2021 |
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