AI-Assisted Multi-Physics Evaluation of Mission Profile-Based Traction Inverter Design for Sustainability

As the global transition toward carbon neutrality accelerates, the sustainability of power electronics has received growing attention from both academia and industry. Nevertheless, standardized methodologies for evaluating the sustainability of power electronic systems—particularly traction inverters—remain limited, largely due to the absence of comprehensive databases and unified assessment frameworks. Leveraging industrial extensive design experience, this paper presents an enhanced methodology for sustainability evaluation of traction inverters. The proposed framework combines advanced component-level modelling with multi-physics-based analysis to more accurately quantify the environmental impacts associated with different power semiconductor technologies. A Random Forest (RF)-based algorithm is employed for junction temperature (TJ) estimation, offering reliable thermal data crucial for sustainability assessment. Experimental validation on a prototype automotive inverter confirms the accuracy and robustness of the RF-based TJ estimation approach, ensuring realistic thermal–environmental coupling within the evaluation workflow. From a thermal perspective, the sizing of power electronics key components (PEKCs) is performed with high precision, enabling a more accurate estimation of power electronics-related material (PERM) usage. Combined with a preliminary CO2-equivalent (CO2e) emissions database, this allows sustainability assessment to be integrated directly into the design stage of the traction inverter. The effectiveness of the proposed approach is demonstrated through a comparative evaluation of three representative inverter topologies.