Early prediction of Lithium-ion cell degradation trajectories using signatures of voltage curves up to 4-minute sub-sampling rates

Rasheed Ibraheem, Yue Wu, Terry Lyons and Gonçalo dos Reis

Applied Energy, 2023, vol. 352, issue C, No S0306261923013387

Abstract: Feature-based machine learning models for capacity and internal resistance (IR) curve prediction have been researched extensively in literature due to their high accuracy and generalization power. Most such models work within the high frequency of data availability regime, e.g., voltage response recorded every 1–4 s. Outside premium fee cloud monitoring solutions, data may be recorded once every 3, 5 or 10 min. In this low-data regime, there are little to no models available. This literature gap is addressed here via a novel methodology, underpinned by strong mathematical guarantees, called ‘path signature’.

Keywords: Capacity degradation; Path signature methodology; Voltage response under constant current at discharge; Lithium-ion cells; Machine learning; Remaining useful life (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (3)

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DOI: 10.1016/j.apenergy.2023.121974

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