State of Health Prediction of Electric Vehicles’ Retired Batteries Based on First-Life Historical Degradation Data Using Predictive Time-Series Algorithms

Farhad Salek (), Shahaboddin Resalati (), Aydin Azizi (), Meisam Babaie, Paul Henshall and Denise Morrey
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Farhad Salek: Faculty of Technology, Design and Environment, Oxford Brookes University, Oxford OX3 0BP, UK
Shahaboddin Resalati: Faculty of Technology, Design and Environment, Oxford Brookes University, Oxford OX3 0BP, UK
Aydin Azizi: Faculty of Technology, Design and Environment, Oxford Brookes University, Oxford OX3 0BP, UK
Meisam Babaie: School of Mechanical Engineering, University of Leeds, Leeds LS2 9JT, UK
Paul Henshall: Faculty of Technology, Design and Environment, Oxford Brookes University, Oxford OX3 0BP, UK
Denise Morrey: Faculty of Technology, Design and Environment, Oxford Brookes University, Oxford OX3 0BP, UK

Mathematics, 2024, vol. 12, issue 7, 1-20

Abstract: The exponential growth of electric and hybrid vehicles, now numbering close to 6 million on the roads, has highlighted the urgent need to address the environmental impact of their lithium-ion batteries as they approach their end-of-life stages. Repurposing these batteries as second-life batteries (SLBs) for less demanding non-automotive applications is a promising avenue for extending their usefulness and reducing environmental harm. However, the shorter lifespan of SLBs brings them perilously close to their ageing knee, a critical point where further use risks thermal runaway and safety hazards. To mitigate these risks, effective battery management systems must accurately predict the state of health of these batteries. In response to this challenge, this study employs time-series artificial intelligence (AI) models to forecast battery degradation parameters using historical data from their first life cycle. Through rigorous analysis of a lithium-ion NMC cylindrical cell, the study tracks the trends in capacity and internal resistance fade across both the initial and second life stages. Leveraging the insights gained from first-life data, predictive models such as the Holt–Winters method and the nonlinear autoregressive (NAR) neural network are trained to anticipate capacity and internal resistance values during the second life period. These models demonstrate high levels of accuracy, with a maximum error rate of only 2%. Notably, the NAR neural network-based algorithm stands out for its exceptional ability to predict local noise within internal resistance values. These findings hold significant implications for the development of specifically designed battery management systems tailored for second-life batteries.

Keywords: EV batteries; second-life batteries; degradation; neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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