Decarbonizing heavy-duty road freight transportation requires efficient energy management in zero-emission powertrains. This study investigates energy management strategies (EMSs) for a heavy-duty Fuel Cell Plug-in Hybrid Electric Vehicle (FC-PHEV). Rather than the typical charge-sustaining operation, these strategies are designed for charge-depleting operation, in which each route begins with a charged battery and ends at a lower state of charge (SOC), leveraging the vehicle’s plug-in capability. The EMSs are evaluated primarily in terms of energy consumption, while battery C-rate and fuel cell ramp rate are used as simple stress indicators for comparative analysis. A backward-facing vehicle model is developed to test several EMSs, including both optimization- and rule-based strategies. The Equivalent Consumption Minimization Strategy (ECMS) emerged as a promising option, motivating further testing with a forward-facing model and additional drive cycles. The simulation results show that ECMS consumed only 1.1% more energy than the global optimal solution found by Pontryagin’s Minimum Principle (PMP) and 7.5% less energy than a simple rule-based strategy, on average across five drive cycles. These results show that ECMS can be effective for a heavy-duty FC-PHEV operating in charge-depleting mode, extending its demonstrated applicability beyond charge-sustaining and light-duty vehicles.

The baseline assessment analysis for total cost of ownership of the pilot demonstrations of the ESCALATE project was carried out for four different powertrain configurations, dealing with modular and scalable powertrains for various vehicle configurations in long-haul trucking. The baseline TCO methodology and results for battery electric trucks (BETs), fuel cell electric trucks (FCETs) and FC range-extending BETs are analysed based on the final designs of the demonstrator vehicles and their foreseen pilot use cases and operational scenarios. As real operation data is not yet available, the analysis relies on energy use and pilot mission analysis through simulation. Overall, the TCO analysis shows several key factors affecting the relative competitiveness of the different zero-emission powertrains and vehicles. Long-haul operations pose clear challenges to vehicle design and long-range vehicles on single charge or refill show increased curb weight, limiting allowable payload due to GVW limits. The best payload capacity is shown for opportunity charging BETs and FCETs. BETs are generally the closest competitor to conventional trucks, but a key factor is the relative energy price difference between diesel, electricity (private or public) and hydrogen. Energy sourcing will be an important factor for end users to enable competitive shift to zero-emission options. Access to cheap private electricity or local green hydrogen may facilitate a choice between the options.

Decarbonizing heavy-duty road transport requires a large-scale transition to zero-emission powertrains and the deployment of necessary charging and hydrogen infrastructure. Such a systemic shift depends on accurate, technology-specific knowledge of real vehicle energy consumption, yet empirical data for zero-emission heavy-duty vehicles-particularly Nordic high-capacity transport (HCT) combinations-remain limited. A central technological question for operators and planners is whether battery-electric (BEV) or fuel-cell electric (FCEV) architectures offer the optimal balance between energy efficiency, operational range and flexibility, and payload capability. In this study, physics-based simulations of eight representative heavy-duty vehicle categories are used to evaluate the energy consumption of BEV and FCEV configurations. The powertrains were dimensioned using VECTO long-haul requirements, and results show that BEVs require large battery capacities for long-distance operation, reducing payload, whereas urban-delivery missions enable substantially smaller battery packs with conventional-truck payload levels. FCEVs maintain high payload across all categories. A real Finnish long-haul case (520 km) demonstrates strong sensitivity to ambient temperature, confirming the need to model driving resistance components-rolling resistance and aerodynamic drag-with sufficient environmental fidelity. Validation with measured data from delivery and long-haul vehicles confirms that the modelling framework reliably reproduces real-world trends.