Multi-objective optimization methodology to select electric motor and battery pack using real drive-cycles

The present manuscript proposes a multi-objective optimization procedure to choose of the optimal E-motor with batteries, with reference by vehicle dynamics, efficiencies, and vehicle behaviour, in accordance with a list of drive-cycles. The implementation of a multi-objective optimization strategy facilitates the selection of the optimal battery by considering a multitude of criteria, including cost, energy density, mass, volume, and others. The flexibility afforded by the method allows the weights to be adjusted according to the priority of each component, thus enabling adaptation to different needs or scenarios. Moreover, the optimization methodology allows direct comparison between batteries and other units and scales, thereby providing a robust and flexible approach. The results of the optimization process are validated through simulation tests conducted using MATLAB/Simulink software. Three case studies are examined: (1) an E-bus, (2) an self-driving car, and (3) a battery electric vehicle (BEV) with ICE chassis utilizing various industry components. In addition to these case studies, this study also evaluates the effect of battery-pack and E-motor layouts, as well as driving conditions, on the behavior and energy consumption of BEV.