Li, Y, Li, K orcid.org/0000-0001-6657-0522, Liu, X et al. (5 more authors) (2022) A hybrid machine learning framework for joint SOC and SOH estimation of lithium-ion batteries assisted with fiber sensor measurements. Applied Energy, 325. 119787. ISSN 0306-2619
Abstract
Accurate State of Charge (SOC) and State of Health (SOH) estimation is crucial to ensure safe and reliable operation of battery systems. Considering the intrinsic couplings between SOC and SOH, a joint estimation framework is preferred in real-life applications where batteries degrade over time. Yet, it faces a few challenges such as limited measurements of key parameters such as strain and temperature distributions, difficult extraction of suitable features for modeling, and uncertainties arising from both the measurements and models. To address these challenges, this paper first uses Fiber Bragg Grating (FBG) sensors to obtain more process related signals by attaching them to the cell surface to capture multi-point strain and temperature variation signals due to battery charging/discharging operations. Then a hybrid machine learning framework for joint estimation of SOC and capacity (a key indicator of SOH) is developed, which uses a convolutional neural network combined with the Gaussian Process Regression method to produce both mean and variance information of the state estimates, and the joint estimation accuracy is improved by automatic extraction of useful features from the enriched measurements assisted with FBG sensors. The test results verify that the accuracy and reliability of the SOC estimation can be significantly improved by updating the capacity estimation and utilizing the FBG measurements, achieving up to 85.58% error reduction and 42.7% reduction of the estimation standard deviation.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2022 Elsevier Ltd. This is an author produced version of an article published in Applied Energy. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Battery capacity; State of charge; Lithium-ion batteries; Joint estimation; Fiber optic sensor |
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) > Institute of Communication & Power Networks (Leeds) |
Funding Information: | Funder Grant number SP Transmission PLC Not Known EPSRC (Engineering and Physical Sciences Research Council) EP/R030243/1 |
Depositing User: | Symplectic Publications |
Date Deposited: | 12 Aug 2022 08:08 |
Last Modified: | 22 Aug 2023 00:13 |
Status: | Published |
Publisher: | Elsevier |
Identification Number: | 10.1016/j.apenergy.2022.119787 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:189851 |