Artificial Intelligence-Driven Thermal Management in Electric Vehicle Traction Inverters: Cooling System Optimization and Real-Time Temperature Prediction

The accelerated adoption of electric vehicles (EVs) has intensified technical demands on traction inverters, particularly regarding power density, energy efficiency, and thermal management. Silicon Carbide (SiC) MOSFET modules, with their superior switching performance and thermal characteristics, offer significant advantages but introduce new thermal challenges due to their increased power density and compact packaging. The primary objective of this study is to enhance real-time thermal control and prediction in SiC-based dual traction inverters through the design and experimental validation of a high-performance cooling system integrated into a modular electric drive platform. Multiple cold plate configurations were evaluated using computational fluid dynamics (CFD) simulations, focusing on junction temperature, thermal uniformity, and pressure drop. To enable real-time thermal predictions, a reduced-order model (ROM) was derived from CFD data and combined with an artificial neural network (ANN) trained to estimate junction temperatures dynamically. The ANN was integrated into the inverter control system and supported by real-time sensor data, enabling continuous dynamic thermal monitoring during operation. The proposed approach offers a scalable and intelligent thermal management solution, supporting predictive maintenance and enhancing the reliability and efficiency of next-generation EV powertrains.