Modelling, Simulation and Implementation of Temperature-Influenced Inverter Performance

Abstract

Power electronic inverters are widely used in electric vehicles, renewable energy systems, industrial automation, and smart power applications due to their high efficiency and fast switching capabilities. However, the performance and reliability of these inverters are significantly affected by temperature variations, especially within semiconductor switching devices such as insulated-gate bipolar transistors (IGBTs). Excessive operating temperature can lead to increased power losses, reduced efficiency, accelerated component degradation, and eventual system failure. Therefore, accurate thermal modelling and effective temperature management are essential for improving inverter performance and operational lifespan.This research focuses on the modelling, simulation, and implementation of a temperature-influenced inverter performance framework aimed at enhancing the thermal reliability and efficiency of inverter systems. The study employs a multi-domain electro-thermal modelling approach that combines analytical power loss calculations, zero-dimensional (0D) thermal network modelling, and finite element analysis (FEA) to evaluate the thermal behavior of inverter components under varying operating conditions. A high-fidelity Digital Twin model is also developed to enable real-time thermal monitoring and predictive analysis of inverter performance.To validate the proposed framework, practical hardware implementation and experimental measurements were carried out, and the obtained results were compared with simulation outputs. The results showed close agreement between simulated and experimental data with minimal deviation, confirming the accuracy and effectiveness of the developed models. The study further demonstrates that proactive thermal management significantly improves inverter efficiency, reduces thermal stress on semiconductor devices, and enhances system reliability. The proposed methodology therefore provides an effective solution for the design and optimization of next-generation temperature-aware intelligent inverter systems.