Multi-Objective Parametric Analysis of EV Traction Inverter between Reliability and Efficiency

Abstract

Transportation electrification is rapidly gaining momentum to reduce greenhouse gas emissions and carbon foot-prints. To help accelerate a swift transition to decarbonization, alternative modes of transportation, such as electric vehicles (EV) must provide superior performance and competitive advantages in regards to reliability (longevity), efficiency (fuel economy), and volumetric energy or power density (compact integration) in contrast to fossil fuel-powered transports. However, achieving optimum designs is challenging due to the multiple physical domain interactions between thermal, electrical, and mechanical systems within an EV drivetrain. Hence, this paper focuses on the multi-parametric design analysis of the EV traction inverter system to perform trade-off studies between two competing objectives: reliability and efficiency. A seamless performance evaluation process was developed between PLECS, a simulation platform for power electronic systems and the optimization computation of genetic algorithm based on NSGA-II in Python to achieve a reliable repetition of varied operating modes of the inverter to seek optimized parameters and non-dominant solutions. A realistic, high-fidelity, and multi-domain EV model based on the known physical parameters of Nissan Leaf was developed in PLECS along with a dynamic driving profile. The paper further discusses parametric design analysis and comparison based on different power module materials and operating conditions, such as EV battery voltage and power module switching frequency. The simulation results show that an optimized SiC solution can provide a higher efficiency design whereas higher reliability can be expected with the optimized IGBT-based designs.