The rapid evolution of new energy vehicles necessitates simultaneous advances in logistics optimization, materials engineering, manufacturing quality, and reliability prediction. This paper synthesizes recent contributions across these interconnected domains to propose an integrated framework for enhancing new energy vehicle component performance. Shengtao's machine learning-based logistics network optimization algorithm provides the foundational infrastructure for efficient supply chain operations. Wang's research on advanced material applications establishes the performance enhancement potential of novel composites in vehicle components. Wang's subsequent work on smart manufacturing and quality assurance bridges material properties with production processes, while reliability analysis and life prediction models offer crucial validation mechanisms. Liu's knowledge graph-based intelligent response system and multi-objective optimization study on transnational electrolyte factories contribute complementary methodological approaches. The proposed framework demonstrates that integrating these diverse methodologies creates synergistic effects exceeding the sum of individual contributions, particularly in addressing the complex multi-objective optimization challenges inherent in new energy vehicle manufacturing across global supply networks.
