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Accelerated state of health estimation of second life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine learning techniques
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Faraji-Niri, Mona, Rashid, Muhammed, Sansom, Jonathan, Sheikh, Muhammed, Widanage, Dhammika and Marco, James (2023) Accelerated state of health estimation of second life lithium-ion batteries via electrochemical impedance spectroscopy tests and machine learning techniques. Journal of Energy Storage, 58 . 106295. doi:10.1016/j.est.2022.106295 ISSN 2352-152X.
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WRAP-Accelerated-state-of-health-estimation-of-second-life-batteries-via-electrochemical-impedance-spectroscopy-tests-Marco-2022.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1738Kb) | Preview |
Official URL: https://doi.org/10.1016/j.est.2022.106295
Abstract
Estimating the State of health (SoH) of Lithium-ion (Li-ion) batteries is a challenging task due to cross coupling and dependency between ageing mechanisms. An accurate estimation is particularly essential for second-life batteries to facilitate their successful implementation in the new application. By adopting the Electrochemical Impedance Spectroscopy (EIS) test and a machine learning (ML) approach, the accelerated SoH estimation problem is studied here. For this purpose, 325 experiments for 30 Li-ion cells were conducted at various SoH, temperature, and state of charge. First an optimised Gaussian Process Regression model is developed and validated for SoH estimation. Then the sensitivity of the model is evaluated relative to measurement noise. Finally, the model's robustness is quantified through a case study involving cells that have been characterised with different physical test equipment. The results demonstrate that the model can predict the SoH of Li-ion cells with an error about 1.1% and is reasonably robust to the various testing conditions of the battery. The methodology for handling the EIS data within a machine learning framework, the sensitivity analysis and the robustness quantification techniques are the main novelties of this study in the context of grading Li-ion batteries for second-life applications
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > QD Chemistry T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Library of Congress Subject Headings (LCSH): | Lithium ion batteries , Lithium ion batteries -- Design and construction, Lithium ion batteries -- Design and construction -- Simulation methods, Impedance spectroscopy , Energy storage, Lithium ion batteries -- Deterioration | ||||||||
Journal or Publication Title: | Journal of Energy Storage | ||||||||
Publisher: | Elsevier | ||||||||
ISSN: | 2352-152X | ||||||||
Official Date: | February 2023 | ||||||||
Dates: |
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Volume: | 58 | ||||||||
Article Number: | 106295 | ||||||||
DOI: | 10.1016/j.est.2022.106295 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 6 December 2022 | ||||||||
Date of first compliant Open Access: | 12 December 2023 | ||||||||
RIOXX Funder/Project Grant: |
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