Multi-Objective Optimization of Battery Cooling Performance and Energy Efficiency in Electric Vehicles

Abstract

Efficient thermal management is essential for improving the safety, performance, and lifespan of lithium-ion batteries used in electric vehicles. Conventional battery cooling systems primarily focus on reducing battery temperature without simultaneously considering cooling energy consumption and overall vehicle efficiency. Multi-objective optimization techniques provide an effective approach for balancing conflicting objectives such as thermal regulation, cooling performance, battery lifespan, and energy efficiency. This paper presents a Multi-Objective Optimization framework integrating machine learning, predictive thermal analytics, intelligent cooling control, and cloud-enabled battery monitoring for high-performance electric vehicle battery systems. The proposed methodology continuously evaluates battery operating conditions and dynamically optimizes cooling strategies to maintain thermal stability while minimizing auxiliary power consumption. Experimental evaluation demonstrates significant improvements in temperature uniformity, cooling efficiency, battery safety, charging performance, energy utilization, and battery lifespan. The proposed optimization framework offers a scalable, intelligent, and energy-efficient solution for sustainable electric vehicle battery thermal management.