AI-Optimized Thermal Management Systems in Autonomous Electric Transit: Enhancing Battery Efficiency for Zero-Emission Urban Mobility

The rapid proliferation of autonomous electric transit networks in modern smart cities introduces unprecedented heavy energy demands, particularly regarding the thermal regulation of high-capacity battery packs under dynamic urban loads. Traditional battery thermal management systems (BTMS) rely predominantly on static, rule-based mechanical cooling mechanisms, which exhibit profound mechanical inefficiencies by continuously operating fluid pumps and fans at nominal speeds regardless of instantaneous thermal flux. To address this critical energy waste, this paper proposes the implementation of an AI-driven dynamic mechanical cooling architecture designed to continuously optimize heat dissipation. By integrating machine learning algorithms with real-time mechatronic actuation, the proposed framework dynamically adjusts fluid dynamics, pump speeds, and volumetric airflow in direct response to predictive thermal load modeling. The theoretical methodology combines Computational Fluid Dynamics (CFD) with deep reinforcement learning to model optimal coolant flow rates, effectively satisfying the First and Second Laws of Thermodynamics while minimizing parasitic mechanical losses. Simulation results indicate that replacing static cooling with AI-optimized dynamic thermal management yields substantial projected mechanical energy savings, reducing parasitic power consumption by up to 35.4% while concurrently extending battery lifecycle through precise temperature homogenization. Furthermore, by drastically lowering the peak power load drawn from sustainable city microgrids during high-frequency transit operations, this research directly advances the core objectives of the RC-FBSIC 2026 "Sustainable Engineering" track. Ultimately, the integration of AI-optimized thermodynamics and mechatronic systems offers a scalable, highly efficient paradigm for achieving zero-emission urban mobility, bridging the gap between mechanical hardware limitations and software-defined energy optimization to enhance the resilience of future municipal energy infrastructures.