Wahiba Yaïci

Decarbonizing heavy-duty road transport requires comparing zero-emission options to guide infrastructure investments along strategic corridors. This study develops a scenario-based techno-economic model to evaluate hydrogen fuel cell trucks (HFCTs) and battery electric trucks supported by dynamic wireless power transfer (DWPT) on a 100 km segment of Italy’s A4 motorway in 2030 and 2050 scenarios. The framework integrates traffic flows, vehicle archetypes, infrastructure sizing, and end-to-end energy chains (power-to-hydrogen-to-wheel for hydrogen and grid-to-wheel for WPT) to estimate capital and operating costs, efficiencies, and energy demand. Results show that hydrogen refueling infrastructure requires lower initial investment (approximately €60 million CAPEX and €20 million annual OPEX) than wireless charging systems (€80 million CAPEX and €15 million OPEX). However, WPT achieves significantly higher grid-to-wheel efficiency (96% vs. 62%) and lower per-vehicle energy demand (18 MWh/year vs. 25 MWh/year). These findings highlight a fundamental trade-off: hydrogen solutions offer operational flexibility and are better suited to long-haul or low-density contexts, while WPT systems are more efficient and become increasingly competitive in high-traffic corridors with high infrastructure utilization. Overall, the results suggest that no single technology universally dominates and that optimal deployment depends on traffic density, infrastructure usage, and system integration. A combined implementation of hydrogen and wireless charging technologies may provide the most effective pathway to balance efficiency, flexibility, and cost in future heavy-duty transport systems.

This paper presents an enhanced computational framework for estimating the energy consumption of Electric School Buses (ESBs) operating under Canadian climatic and traffic conditions. Building on previous modeling efforts, the proposed approach integrates real-world traffic data, auxiliary load scenarios, and a physics-based energy consumption model to quantify the impact of operational variables on State-of-Charge (SOC) depletion. To address gaps in prior studies, the model incorporates a multi-scenario sensitivity analysis, a clear discussion of methodological assumptions, and a comparative validation using published telemetry-based energy benchmarks from similar electric bus platforms. A case study in Ottawa’s Altavista neighborhood demonstrates the applicability of the framework, evaluating the Lion C ESB across urban and extra-urban segments. Results show that auxiliary loads are a primary driver of consumption in low-speed conditions, increasing total SOC depletion by over 36% between mild and winter like scenarios. The study highlights implications for fleet operations, HVAC investment decisions, and route planning, providing actionable insights for school districts deploying electric fleets. Limitations and directions for future model extensions such as multi-bus routing and stochastic weather conditions are also explicitly discussed.

This research evaluates how wind and solar PV systems balance together. Increasing the share of stochastic renewable energy production in electricity and hot turning reserve deficit are welcome compensation issues. This research used weather station data from an open seashore from the last 10 years, 2014–2023, on the Estonian island Saaremaa’s west coast to evaluate yearly fluctuations. We used the indicator demand cover factor to estimate the coincidence of wind generation and solar PV system electricity. For clarity, the initial data were prepared by assuming the equality of production and consumption annual data by scaling the obtained data. This study demonstrates that the best compensating possibilities are the share of wind generation and solar PV electricity mix, respectively, equal to 0.7/0.3 and 0.8/0.2, reaching a demand cover factor of 0.62. This study evaluated the demand cover factor’s dependence on increased production compared to consumption. This study used different batteries to research the influence of these demand cover factors. Furthermore, this research makes a significant contribution by showcasing how to turn weather station data into real wind generator and PV panel production data.