Machine Learning-Driven Optimization and Reliability Enhancement in New Energy Vehicle Component Manufacturing

Abstract

The rapid evolution of new energy vehicles necessitates continuous innovation in manufacturing processes, logistics optimization, and component reliability. This paper synthesizes recent advancements in machine learning-based logistics network optimization, advanced material applications, intelligent manufacturing quality assurance, reliability analysis and life prediction models for new energy vehicle components. Furthermore, it explores synergistic applications of knowledge graph-based intelligent response systems and multi-objective process parameter optimization from adjacent industrial domains. The integration of these methodologies presents a comprehensive framework for enhancing the performance, durability, and production efficiency of new energy vehicle systems. This review identifies critical interdependencies between algorithmic optimization and materials engineering while proposing future research directions that bridge these disciplines.