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Thermal monitoring modeling of solid-state batteries: Decoding temperature inhomogeneity via machine learning of interfacial heat generation

Author

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  • Liu, Wenzhuo
  • Liu, Zhe
  • Lv, Song

Abstract

Solid-state batteries (SSBs) are promising candidates for next-generation energy storage devices, leveraging lithium metal anodes to achieve higher energy density and enhanced safety. However, nonuniform temperature distributions under high-rate cycling significantly impact the cycle life and rate capability of SSBs, thereby limiting their widespread application. These macroscopic performance variations arise from microstructural heterogeneities at solid–solid interfaces, which induce complex nonlinear pathways leading to thermal inhomogeneity. Consequently, elucidating the mechanisms by which solid–solid interfaces influence thermal behavior and establishing their nonlinear correlations necessitate an integrated characterization and modeling approach. In this work, we present a machine learning-assisted modeling framework that decodes the relationship between interfacial nonlinear heat generation and spatial thermal inhomogeneity in SSBs. Morphological characterizations, including SEM observations, reveal localized structural changes and interfacial cracking in regions of high temperature. Based on these findings, an equivalent circuit model (ECM) guided by electrochemical impedance spectroscopy (EIS) is developed, and a nonlinear heat generation model is constructed using a CNN–LSTM–Transformer architecture. The model accurately captures the spatially resolved, interface-driven nonlinear heat behavior and predicts two-dimensional thermal distributions with high fidelity. The overall prediction accuracy reaches 99.84%, with a maximum deviation below 0.56 °C. This approach provides a numerical foundation for the study of nonlinear interfacial heat generation, allowing quantification of the heat generation rates associated with different solid electrolytes and interface engineering strategies, and supports the development of safer, higher-energy-density solid-state electrolytes and interface designs.

Suggested Citation

  • Liu, Wenzhuo & Liu, Zhe & Lv, Song, 2026. "Thermal monitoring modeling of solid-state batteries: Decoding temperature inhomogeneity via machine learning of interfacial heat generation," Applied Energy, Elsevier, vol. 403(PA).
  • Handle: RePEc:eee:appene:v:403:y:2026:i:pa:s0306261925017672
    DOI: 10.1016/j.apenergy.2025.127037
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