Reinforcement Learning Control of Two-Phase Cooling Systems for Next Generation Power Converters

Effective thermal management of SiC, GaN, and silicon devices in power electronics presents significant challenges due to their complex thermal behavior. Two-Phase Cooling (TPC) systems have emerged as a promising solution due to their superior efficiency in heat dissipation. However, managing these systems remains difficult, as traditional industrial control methods are often insufficient, and adaptive or predictive control techniques require substantial computational resources. This issue is further compounded in Electric Vehicle Charging Stations, where high power demand and thermal management play a critical role in system reliability and performance. This paper addresses these challenges by introducing a novel control strategy based on Reinforcement Learning (RL) for TPC systems in power electronics. The results highlight the potential of this RL-based approach in overcoming the limitations of existing methods, offering a more efficient solution for managing the thermal behavior of power electronics devices.