Research on Optimization Design of Cold Resistant New Energy Vehicle Battery Pack Structure Based on Quantum Computing

Cold climates reduce battery efficiency in new energy vehicles (NEVs), necessitating optimized thermal-resistant designs leveraging advanced quantum and classical optimization techniques. Existing approaches struggle with optimizing complex, multi-objective battery pack designs for cold climates, often lacking efficiency, scalability, and integration of advanced computational techniques like quantum optimization for improved results. To develop an advanced optimization framework for designing cold-resistant battery packs that enhance energy efficiency, thermal stability, and structural durability under extreme cold conditions, ensuring improved performance and reliability for NEVs operating in challenging climates. The proposed cold-resistant new energy vehicle battery pack structure (CRNEV-BPS) framework optimizes cold-resistant battery pack design through initial parameter setup, and hybrid optimization using quantum and Bayesian methods to enhance energy efficiency and thermal stability. In the parameter setup and design for the cold-resistant battery pack, lithium iron phosphate (LiFePO 4 ) is used for stable cell performance, graphite for heat dissipation, aluminum alloys for a lightweight structure, silicon-based semiconductors for efficient power management, and copper for electrical conductivity. The quantum approximate mutated Bayesian algorithm (QAM-BO) combines quantum computing’s approximation power with Bayesian optimization and mutation techniques to enhance the search for optimal solutions in multifaceted design spaces, improving accuracy (95%). This effectively searches the design spaces that have well-refined battery pack parameters for optimal performance in cold weather. CRNEV-BPS achieves improved energy efficiency, thermal stability, and structural integrity for NEVs in extreme climates.