Computational Micromechanics and Machine Learning-Informed Design of Composite Carbon Fiber-Based Structural Battery for Multifunctional Performance Prediction

Multifunctional structural batteries, which combine load-bearing and energy storage, promise weight reduction and enhanced safety but face commercialization challenges due to vast design spaces and costly trial-and-error. This work accelerates the design of CF-based structural batteries impregnated with SPE using an experimentally validated framework. Finite element analysis based on computational micromechanics examines the CF/SPE interface and predicts effective properties, while a Bayesian-optimized ANN forecasts capacity under rapid degradation conditions—providing promising insights for multifunctional composite optimization.

Mohamad A. Raja et al., ACS Appl. Mater. Interfaces 2025, 17, 13, 20125–20137
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