Deep Mining on the Formation Cycle Features for Concurrent SOH Estimation and RUL Prognostication in Lithium-Ion Batteries

Dongchen Yang, Weilin He and Xin He ()
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Dongchen Yang: Faculty of Engineering, Architecture and Information Technology (EAIT), University of Queensland, Brisbane, QLD 4072, Australia
Weilin He: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xin He: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2025, vol. 18, issue 8, 1-15

Abstract: Lithium-ion batteries (LIBs) are widely utilized in consumer electronics, electric vehicles, and large-scale energy storage systems due to their high energy density and long lifespan. Accurately estimating the state of health (SOH) and predicting the remaining useful life (RUL) of cells is crucial to ensuring their safety and preventing potential risks. Existing state estimation methodologies primarily rely on electrical signal measurements, which predominantly capture electrochemical reaction dynamics but lack sufficient integration of thermomechanical process data critical to holistic system characterization. In this study, relevant thermal and mechanical features collected during the formation process are extracted and incorporated as additional data sources for battery state estimation. By integrating diverse datasets with advanced algorithms and models, we perform correlation analyses of parameters such as capacity, voltage, temperature, pressure, and strain, enabling precise SOH estimation and RUL prediction. Reliable predictions are achieved by considering the interaction mechanisms involved in the formation process from a mechanistic perspective. Full lifecycle data of batteries, gathered under varying pressures during formation, are used to predict RUL using convolutional neural networks (CNN) and Gaussian process regression (GPR). Models that integrate all formation-related data yielded the lowest root mean square error (RMSE) of 2.928% for capacity estimation and 16 cycles for RUL prediction, highlighting the significant role of surface-level physical features in improving accuracy. This research underscores the importance of formation features in battery state estimation and demonstrates the effectiveness of deep learning in performing thorough analyses, thereby guiding the optimization of battery management systems.

Keywords: lithium-ion batteries; state of health; remaining useful life; estimation; formation features (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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