Enhancing Battery SOH Prediction with Physics-Informed Neural Networks in Data-Scarce Environments
Published in Energy, 2025
This study presents a novel approach to predict battery State of Health (SOH) using Physics-Informed Neural Networks (PINNs) in environments where data is limited. The methodology combines data-driven learning with physical constraints to improve prediction accuracy and reliability.
Authors: Seo Y., Kim T., Barde S.
Published in: Energy, Volume 335, 2025
DOI: 10.1016/j.energy.2025.138316
Recommended citation: Seo Y., Kim T., & Barde, S. (2025). "Enhancing Battery SOH Prediction with Physics-Informed Neural Networks in Data-Scarce Environments." Energy. 335, 138316.
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