Study to determine the efficiency of thermal management systems in batteries

Abstract

This research focuses on the development and evaluation of the efficiency of an Intelligent Battery Thermal Management System (iBTMS) for electric vehicles in the 21st century. The study integrates multiple advanced technologies, including artificial intelligence, digital twin modeling, quantum optimization algorithms, and intelligent sensor systems. The main objective is to address thermal management issues, which remain critical limitations in enhancing electric vehicle performance and extending battery lifespan. An experimental research methodology combined with computer simulation programs was employed to assess the performance of the iBTMS compared to traditional systems under various operating conditions.
The research findings indicate a significant superiority of the iBTMS. It was able to improve heat dissipation efficiency by 24% (from 78% to 97%), reduce system energy consumption by 38% (from 450W to 280W), and extend battery life by 42% (from 2,500 cycles to 3,550 cycles) compared to conventional systems. Stability tests under different ambient temperatures (-10°C to 50°C) showed that the iBTMS consistently maintained an efficiency range of 95-98%, while traditional systems exhibited fluctuating efficiency between 35-75%. Furthermore, the digital twin computer model developed in this study demonstrated high accuracy in predicting battery temperature, with an average error of only ±0.3°C, which is considered appropriate for real-time control applications.