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<p>Determining the optimal ceramic content of the ceramics-in-polymer composite electrolytes and the appropriate stack pressure can effectively improve the interfacial contact of solid-state batteries (SSBs).&#xa0;Based on the contact mechanics model and constructed by the conjugate gradient method, continuous convolution, and fast Fourier transform, this paper analyzes and compares the interfacial contact responses involving the polymers commonly used in SSBs, which provides the original training data for machine learning. A support vector regression model is established to predict the relationship between the content of ceramics and the interfacial resistance. The Bayesian optimization and K-fold cross-validation are introduced to find the optimal combination of hyperparameters, which accelerates the training process and improves the model’s accuracy. We found the relationship between the content of ceramics, the stack pressure, and the interfacial resistance. The results can be taken as a reference for the design of the low-resistance composite electrolytes for solid-state batteries.</p>

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Design of Low-Resistance Composite Electrolytes for Solid-State Batteries Based on Machine Learning

  • 期刊论文

  • 发表日期：

    2025年1月16日

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  • Yu Xiong,
  • Zizhang Lin,
  • Jinxing Li,
  • Zijian Li,
  • Ao Cheng,
  • Xin Zhang

摘要

Determining the optimal ceramic content of the ceramics-in-polymer composite electrolytes and the appropriate stack pressure can effectively improve the interfacial contact of solid-state batteries (SSBs). Based on the contact mechanics model and constructed by the conjugate gradient method, continuous convolution, and fast Fourier transform, this paper analyzes and compares the interfacial contact responses involving the polymers commonly used in SSBs, which provides the original training data for machine learning. A support vector regression model is established to predict the relationship between the content of ceramics and the interfacial resistance. The Bayesian optimization and K-fold cross-validation are introduced to find the optimal combination of hyperparameters, which accelerates the training process and improves the model’s accuracy. We found the relationship between the content of ceramics, the stack pressure, and the interfacial resistance. The results can be taken as a reference for the design of the low-resistance composite electrolytes for solid-state batteries.