Scenario-based multidisciplinary optimization for a new accelerated life testing of electric traction motor and inverter system.

With the electrification of automobiles, the importance of an electric traction motor and inverter system is increasing. Durability and reliability tests are crucial in the development process of electric vehicle (EV) systems. To reduce the time and cost of durability and reliability tests, accelerated life testing (ALT) that applies high-stress conditions in a short time needs to be carried out. Because the electric traction motor and inverter system have been combined as vehicles have become smaller, it is necessary to concurrently test these parts. This study proposes a scenario-based multidisciplinary optimization (SBMO) method to develop a new ALT that simultaneously assesses the mechanical damage to the electric traction motor and the electrical damage to the inverter system. First, four driving scenarios for the ALT are extracted by analyzing the driving conditions of various field tests. Second, a methodology for EV modeling and lifespan prediction of the electric traction motor and inverter system based on the analytical mechanics is proposed. Third, discrete scenario variables corresponding to the four driving scenarios are defined. Fourth, a new SBMO problem is formulated to generate a new ALT. The test requirements of an ALT are reflected in the SBMO constraints to be employed in the development of EVs. Finally, a genetic algorithm is used to solve the SBMO problem. The SBMO successfully obtains the optimum ALT cycle satisfying the test requirements for designing an electric traction motor and inverter system in the early design stage. [ABSTRACT FROM AUTHOR]