Fuel Cell Hybrid Electric Vehicles: methodological approach for sizing and control design

Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer a promising route to the decarbonization of transport sector by coupling high conversion efficiency with long range and zero tailpipe emissions. However, designing hybrid powertrains remains challenging, as coordinated sizing and control are crucial to fully exploit their potential. A co-optimization framework is therefore essential due to the strong interdependence between these subsystems. This work develops a structured application-oriented methodological framework for FCHEV powertrain design, integrating both sizing and control aspects. The method is applied to a hybrid electric light quadricycle. A high-fidelity virtual test bench, validated at component and system level through an in-house Hardware-in-the-Loop platform, ensures reliable performance analysis under diverse conditions. A scale-up approach within the virtual test bench assesses Fuel Cell Stack (FCS) scalability and its impact on vehicle performance. The need for co-optimization is demonstrated by comparing two Energy Management Strategies (EMSs): a baseline fuzzy logic controller and a novel Driving Pattern Recognition (DPR)-based EMS. The latter, trained via optimal results, adapts in real-time to driving conditions, achieving near-optimal performance with low computational cost. Four FCS sizes, corresponding to scale-up factors of 1, 1.5, 2 and 2.5, were tested on a random driving cycle, showing that, among the investigated candidates, the preferred design combines a mid-sized FCS with the DPR-based EMS, balancing performance and cost. The DPR controller outperformed the baseline by 7–8% in mileage, confirming the value of co-optimized design. The study establishes a scalable, experimentally validated methodology for hybrid powertrain design, paving the way for integration of hydrogen-based propulsion in the future micromobility ecosystem.