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Unlocking the potential of second-life batteries: A nonlinear frequency and machine learning approach to health prediction

Lookup NU author(s): Dr Ma'd El DalahmehORCiD, Dr Jie ZhangORCiD, Professor Mohamed MamloukORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

© 2026 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license. http://creativecommons.org/licenses/by/4.0/AbstractThe growing demand for sustainable energy solutions highlights the need to extend the use of lithium-ion batteries (LIBs) in first-life applications (e.g., electric vehicles) or repurpose them for second-life uses like energy storage. However, most existing research primarily focuses on first-life applications, with limited attention to the unique challenges of second-life batteries, where accurate estimation of the State of Health (SOH) at low levels (<80%) becomes increasingly difficult due to nonlinear degradation mechanisms. This study addresses this gap by introducing a machine learning approach leveraging Nonlinear Frequency Response Analysis (NFRA) data for SOH estimation in second-life applications down to 60%. NFRA outperformed traditional Electrochemical Impedance Spectroscopy (EIS), achieving >98% prediction accuracy for second-life batteries, even when trained on first-life data alone, and >96.7% using published datasets. NFRA captures nonlinear responses, such as energy losses linked to Li+ transport and solid-electrolyte interface dynamics, which EIS fails to detect. Two predictive models, Long Short-Term Memory (LSTM) networks and Nonlinear Autoregressive with External Input (NARX), were tested, with LSTM reducing root mean square error (RMSE) by up to 30% compared to NARX. NFRA consistently reduced RMSE by over 39% relative to EIS in second-life phases. These findings establish NFRA as a reliable tool for enhancing SOH predictions, enabling safer, more efficient battery repurposing and extended lifetimes.

Publication metadata

Author(s): El-Dalahmeh M, Hui Z, Zhang J, Mamlouk M

Publication type: Article

Publication status: Published

Journal: Journal of Energy Storage

Year: 2026

Volume: 159

Print publication date: 30/05/2026

Online publication date: 27/03/2026

Acceptance date: 19/03/2026

Date deposited: 13/04/2026

ISSN (print): 2352-152X

ISSN (electronic): 2352-1538

Publisher: Elsevier Ltd

URL: https://doi.org/10.1016/j.est.2026.121714

DOI: 10.1016/j.est.2026.121714

Data Access Statement: full impedance dataset (EIS and NFRA measurements from the five tests) and the source code used for the LSTM and NARX modelling are openly available at [https://figshare.com/s/e8a5b1687820dc01e18e]

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