Shalini Dwivedia b, Aparna Akulaa b, and Michael G. Pechtc
aCSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, 160030, India
bAcademy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India
cCenter for Advanced Life Cycle Engineering (CALCE), University of Maryland, College Park, MD, 20742, USA
For more information about this article and related research, please contact Prof. Michael Pecht.
Abstract:
The capacity degradation of lithium-ion batteries occurs both during storage and operational usage. This paper investigates the capacity degradation of lithium-ion batteries during storage (calendar ageing) by analysing the interplay of storage temperature, state-of-charge (SOC), and time. Leveraging the machine learning techniques of Gaussian process regression and extreme gradient boosting (XGBoost), a predictive model is developed to characterize the degradation patterns. The study includes a sensitivity analysis of stress factors to identify their relative impact on degradation. The insights gained from this analysis are utilized to recommend optimal storage conditions, offering practical guidance for enhancing the durability and performance of lithium-ion batteries in real-world applications.
This article is available for free online here until October 23, 2024.
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