Electrochemo-mechanical response of all solid-state batteries: Finite element simulations supported by image-based 3D reconstruction of X-ray microscopy tomography

Huang, Pingyuan; Gao, Li; Guo, Zhan‐Sheng

doi:10.1016/j.electacta.2023.142873

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2024

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Cited by 6 publications

(1 citation statement)

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“…FEM also provides valuable insights into the structural integrity, thermal management and electrochemical performance of SSEs, and greatly aids in the design and optimization of SSE architectures and geometries to enhance their efficiency and performance. 55 FEM offers a comprehensive understanding of the multi-physics behaviors, i.e. , electrochemical, thermal and mechanical, exhibited by SSE systems.…”

Section: Numerical Approaches At Various Scalesmentioning

confidence: 99%

Computational approach inspired advancements of solid-state electrolytes for lithium secondary batteries: from first-principles to machine learning

Zheng,

Zhou,

Zhu

2024

Chem. Soc. Rev.

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Section: Numerical Approaches At Various Scalesmentioning

confidence: 99%

Computational approach inspired advancements of solid-state electrolytes for lithium secondary batteries: from first-principles to machine learning

Zheng,

Zhou,

Zhu

2024

Chem. Soc. Rev.

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Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Ji,

Zhu,

Yang

et al. 2024

Small Methods

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Battery characterization and prognosis are essential for analyzing underlying electrochemical mechanisms and ensuring safe operation, especially with the assistance of superior data‐driven artificial intelligence systems. This review provides a unique perspective on recent progress in data‐driven battery characterization and prognosis methods. First, recent informative image characterization and impedance spectrum as well as high‐throughput screening approaches on revealing battery electrochemical mechanisms at multiple scales are summarized. Thereafter, battery prognosis tasks and strategies are described, with the comparison of various physics‐informed modeling strategies. Considering unlocking mechanisms from tremendous battery data, the dominant role of physics‐informed interpretable learning in accelerating energy device development is presented. Finally, challenges and prospects on data‐driven characterization and prognosis are discussed toward accelerating energy device development with much‐enhanced electrochemical transparency and generalization. This review is hoped to supply new ideas and inspirations to the next‐generation battery development.

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Framework for electrochemical-mechanical behavior of all-solid-state batteries: From the reconstruction method to multi-physics and multi-scale modeling

Huang,

Guo

2024

International Journal of Solids and Structures

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Electrochemo-mechanical response of all solid-state batteries: Finite element simulations supported by image-based 3D reconstruction of X-ray microscopy tomography

Cited by 6 publications

References 67 publications

Computational approach inspired advancements of solid-state electrolytes for lithium secondary batteries: from first-principles to machine learning

Computational approach inspired advancements of solid-state electrolytes for lithium secondary batteries: from first-principles to machine learning

Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Framework for electrochemical-mechanical behavior of all-solid-state batteries: From the reconstruction method to multi-physics and multi-scale modeling

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