The demand for extended electric vehicle (EV) range necessitates advanced lightweighting strategies. This study introduces a materials genome approach, augmented by machine learning (ML), for optimizing lightweight composite designs for EVs. A comprehensive materials genome database was developed, encompassing composites based on carbon, glass, and natural fibers. This database systematically records critical parameters such as mechanical properties, density, cost, and environmental impact. Machine learning models, including Random Forest, Support Vector Machines, and Artificial Neural Networks, were employed to construct a predictive system for material performance. Subsequent material composition optimization was performed using a multi-objective genetic algorithm. Experimental validation demonstrated that an optimized carbon fiber/bio-based resin composite achieved a 45% weight reduction compared to conventional steel, while maintaining equivalent structural strength. The predictive accuracy of the models reached 94.2%. A cost-benefit analysis indicated that despite a 15% increase in material cost, the overall vehicle energy consumption decreased by 12%, leading to an 18% total cost saving over a five-year operational lifecycle, under a representative mid-size battery electric vehicle (BEV) operational scenario.
Liao et al. (Thu,) studied this question.