This study presents a comprehensive methodology for the design and optimization of hybrid electric powertrains across multiple vehicle segments and electrification levels. A full-factorial simulation framework was developed in MATLAB/Simulink, featuring a modular, physics-based vehicle model combined with a backward simulation approach and an ECMS (Equivalent Consumption Minimization Strategy) -based energy management algorithm. The objective is to evaluate three hybrid powertrain architectures, namely Series Hybrid (SH), Series-Parallel Hybrid with a single gear stage (SHP1), and Series-Parallel Hybrid with a double gear stage (SHP2), across three vehicle classes (Sedan, Mid-SUV, Large-SUV), four different internal combustion engines (ICEs), and three application types (HEV, PHEV, REEV).
More than 10,000 unique configurations were simulated and filtered through a two-step performance requirements analysis. The first phase assessed individual vehicle-level performance targets, while the second phase applied combined constraints to identify only those configurations that simultaneously satisfied all criteria. Remaining candidates were then evaluated using a multi-criteria assessment framework, incorporating metrics such as component commonality, fuel and energy consumption, NVH (noise, vibration, and harshness), and cost proxies.
From an architectural perspective, SH required the highest P3 e-machine sizing, while SHP2 allowed for the lowest sizing and most efficient overall system design. SHP1 provided a robust intermediate solution with simplified component scaling.
Final component definitions for each architecture, vehicle type, and application provide a practical reference for future hybrid powertrain development. The proposed framework enables structured trade-off analysis and supports data-driven decisions for scalable and efficient hybrid electric vehicle platforms.