Multiobjective GA Optimization for Energy Efficient Electric Vehicle Drivetrains

This paper investigates the impact of using wide bandgap (WBG) technology-based bidirectional interleaved HV DC/DC converters on the performance of battery electric vehicles (BEVs), An existing electric vehicle is upgraded using off-the-shelf components. There are a variety of batteries, high voltage (HV) DC/DCs, inverters, electric motors, transmissions, etc., available off-the-shelf; hence, numerous possible combinations can be formed, which make the optimal component selection process more complicated through analytical methods. In this paper, a multiobjective genetic algorithm (MOGA) is adopted to minimize the electric energy consumption by improving drivetrain efficiency based on the optimal variant selection of the components. It is found from the virtual simulation framework in MATLAB/Simulink® that overall, there is a 9.2% reduction in the energy consumption over a given driving cycle, i.e., Worldwide Harmonized Light Vehicles Test Procedure-3a (WLTP3a). To this end, the drivetrain performance in terms of acceleration time from 0–90 km/h is also improved by 10.2%, while the efficiency is improved by 1.5% compared to the conventional e-drivetrain.