Abstract
The transition to Hydrogen Fuel Cell Vehicles (HFCVs) is recognized for its potential to eliminate tailpipe emissions and promote cleaner urban mobility. This study examines the impact of varying HFCV adoption rates, as well as the number and location of hydrogen refueling stations, on emissions, driving behavior, and traffic dynamics in urban environments. A hybrid methodology, combining statistical analyses and machine learning techniques, was used to simulate all scenarios in the city of Linz, Austria. The simulation results indicate that the configuration of hydrogen refueling infrastructure, along with smoother driving patterns, can contribute to reduced congestion and significantly lower CO2 emissions in high-traffic urban areas. Increasing the proportion of HFCVs was also found to be beneficial due to their use of electric motors powered by hydrogen fuel cells, which offer features such as instant torque, regenerative braking and responsive acceleration. Although these features are not unique to HFCVs, they contributed to a slight shift in driving behavior toward smoother and more energy-efficient patterns. This change occurred due to improved acceleration and deceleration capabilities, which reduced the need for harsh maneuvers and supported steadier driving. However, the overall effect is highly dependent on traffic conditions and real-world driving behavior. Furthermore, marginal and context-dependent improvements in traffic flow were observed in certain areas. These were attributed to HFCVs' responsive acceleration, which might assist in smoother merging and reduce stop-and-go conditions. These findings provide valuable insights for transportation planners and policymakers aiming to promote sustainable urban development.
| Originalsprache | Englisch |
|---|---|
| Aufsatznummer | 126628 |
| Seitenumfang | 21 |
| Fachzeitschrift | Applied Energy |
| Volume | 401 |
| Ausgabenummer | Spec. Iss. |
| Frühes Online-Datum | 26 Aug. 2025 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 15 Dez. 2025 |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
-
SDG 7 – Erschwingliche und saubere Energie
-
SDG 9 – Industrie, Innovation und Infrastruktur
-
SDG 11 – Nachhaltige Städte und Gemeinschaften
-
SDG 13 – Klimaschutzmaßnahmen
-
SDG 17 Partnerschaften für die Ziele
Wissenschaftszweige
- 211911 Nachhaltige Technologien
- 201305 Verkehrstechnik
- 211908 Energieforschung
- 211909 Energietechnik
- 102001 Artificial Intelligence
- 201306 Verkehrstelematik
- 102019 Machine Learning
- 202041 Technische Informatik
JKU-Schwerpunkte
- Sustainable Development: Responsible Technologies and Management
- Digital Transformation
Dieses zitieren
- APA
- Author
- BIBTEX
- Harvard
- Standard
- RIS
- Vancouver