Multi timescale battery modeling: Integrating physics insights to data-driven model

Tushar Desai, Alexander J. Gallo and Riccardo M.G. Ferrari

Applied Energy, 2025, vol. 393, issue C, No S0306261925007706

Abstract: Developing accurate models for batteries, capturing ageing effects and nonlinear behaviors, is critical for the development of efficient and effective performance. Due to the inherent difficulties in developing physics-based models, data-driven techniques have been gaining popularity. However, most machine learning methods are black boxes, lacking interpretability and requiring large amounts of labeled data. In this paper, we propose a physics-informed encoder–decoder model that learns from unlabeled data to separate slow-changing battery states, such as state of charge (SOC) and state of health (SOH), from fast transient responses, thereby increasing interpretability compared to conventional methods. By integrating physics-informed loss functions and modified architectures, we map the encoder output to quantifiable battery states, without needing explicit SOC and SOH labels. Our proposed approach is validated on a lithium-ion battery ageing dataset capturing dynamic discharge profiles that aim to mimic electric vehicle driving profiles. The model is trained and validated on sparse intermittent cycles (6 %–7 % of all cycles), accurately estimating SOC and SOH while providing accurate multistep ahead voltage predictions across single and multiple-cell based training scenarios.

Keywords: Encoder–decoder; Physics informed machine learning; Multi timescale battery modeling; Sparse-unlabeled data (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.apenergy.2025.126040

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