Evaluating the Competitive Edge of Electric and Hybrid Powertrain Architectures Through MCDM

Abstract

Leading companies worldwide are rapidly advancing their hybridization and electrification efforts to gain a competitive edge. The choice of vehicle powertrain architecture is crucial, as it can determine a company's future. Electric and hybrid vehicles exhibit various powertrain architectural features, each with distinct values for parameters such as Range, Battery Weight, Battery Energy Efficiency, Battery Energy Density, Battery Cost, and Driving Cost. This study evaluates five different vehicle powertrain architectures based on six criteria, providing a decision prediction on the optimal architecture using objective data of 24 vehicles. Criteria weighting was performed using the Entropy method, and similar but slightly changing results were observed using both the AHP and SAW methods for Multi-Criteria Decision-Making (MCDM). The Parallel Hybrid (PH) ranked first by far (27.51 by AHP and 0.92 by SAW), Series-Parallel Hybrid (SPH) and Plug-in Hybrid (PHEV) architectures ranked second (21.63 by AHP and 0.69 by SAW) and third (21.32 by AHP and 0.72 by SAW) respectively with close values. Fully Electric Vehicle (EV) (15.05 by AHP and 0.48 by SAW) and Series Hybrid (SH) (14.49 by AHP and 0.51 by SAW) architectures occupied the last two positions, also with values close to each other. Finally, in order to test whether the analysis rankings were stable, a sensitivity analysis was performed on both increase and decrease directions of the criteria's values separately and according to the results obtained, it was determined that the analysis was stable.