Optimal Graphene Heat Pipe Parameters for Enhanced Battery Thermal Management through Global Optimization

Achieving zero carbon emissions has led to a surge of interest in electric vehicles globally. For electric vehicle batteries, thermal management systems are critical due to tem-perature's impact on charging and discharging performance. This paper proposes an AI-based approach to determine the optimal configuration for heat dissipation in graphene heat pipes. By employing the right parameters, graphene heat pipes outperform traditional cooling systems. However, identifying the optimal configuration can be time-consuming and expensive. We formulate this as a global optimization problem and utilize surrogate models to address this challenge. Our experimental results show significant performance improvements in graphene heat pipes.