Li, Y orcid.org/0000-0002-5521-9224, Li, K, Liu, X et al. (2 more authors) (2021) Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning. Applied Energy, 285. 116410. ISSN 0306-2619
Abstract
Online battery capacity estimation is a critical task for battery management system to maintain the battery performance and cycling life in electric vehicles and grid energy storage applications. Convolutional Neural Networks, which have shown great potentials in battery capacity estimation, have thousands of parameters to be optimized and demand a substantial number of battery aging data for training. However, these parameters require massive memory storage while collecting a large volume of aging data is time-consuming and costly in real-world applications. To tackle these challenges, this paper proposes a novel framework incorporating the concepts of transfer learning and network pruning to build compact Convolutional Neural Network models on a relatively small dataset with improved estimation performance. First, through the transfer learning technique, the Convolutional Neural Network model pre-trained on a large battery dataset is transferred to a small dataset of the targeted battery to improve the estimation accuracy. Then a contribution-based neuron selection method is proposed to prune the transferred model using a fast recursive algorithm, which reduces the size and computational complexity of the model while maintaining its performance. The proposed model is capable of achieving fast online capacity estimation at any time, and its effectiveness is verified on a target dataset collected from four Lithium iron phosphate battery cells, and the performance is compared with other Convolutional Neural Network models. The test results confirm that the proposed model outperforms other models in terms of accuracy and computational efficiency, achieving up to 68.34% model size reduction and 80.97% computation savings.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2021, Elsevier Ltd. All rights reserved. This is an author produced version of an article published in Applied Energy. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Capacity estimation; Convolutional neural networks; Lithium-ion batteries; Network pruning; Transfer learning |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) |
Funding Information: | Funder Grant number EPSRC (Engineering and Physical Sciences Research Council) EP/R030243/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 05 Jan 2021 17:52 |
Last Modified: | 04 Jan 2022 01:38 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.apenergy.2020.116410 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:169492 |