Electrifying Road Freight Transport: A Comparative Study in Finland and Switzerland
Autor: | Jahangir Samet, Mehdi |
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Přispěvatelé: | Tekniikan ja luonnontieteiden tiedekunta - Faculty of Engineering and Natural Sciences, Tampere University |
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Degree Programme in Industrial Engineering and Management
MSc (Tech) Greenhouse Gas Emission Action Costs for CO2 Equivalent Reduction Electric Road Systems Battery Electric Vehicles Battery Electric Vehicle Potential Model Battery Electric Trucks Electrifying Road Freight Transport Life Cycle Assessment |
Popis: | Different low-emission mobility and green logistics strategies have been followed by the European Commission in recent years to reduce greenhouse gas (GHG) emission in the transport sector. On top of all these strategies, milestones and targets have been set for the electrification of road transport in the European roadmap. The range anxiety has been subjected as a matter of debate for electrifying different truck classes by using battery electric vehicles (BEVs). However, the technical battery and fast-charging limits will cause more challenges with the electrification of medium and heavy-duty trucks compared to the light-duty ones. The objective of this thesis is to evaluate the potential of electrifying road freight transport by implementing the battery electric trucks (BETs) in Finland and Switzerland. For this purpose, a three-step framework is suggested to prepare data, analyse electrification potential, and estimate the emission-cost factors. The main resources for the data preparation step are the valuable freight travel datasets, which previously processed by Liimatainen et al. (2019), for Finland and Switzerland in 2016. The data preparation and electrification analysis are customised based on the battery electric vehicle potential (BEVPO) model, developed by Melliger et al. (2018), and the requirement settings in the different scenario packages of battery and fast charging facilities. Finally, the emission-cost analysis step is dedicated for evaluation of the CO2 equivalent life cycle assessment (LCA), total cost of ownership (TCO), and action costs for CO2 equivalent reduction potential in different electrification scenario packages. This study shows that the road electrification potential in Finland is limited based on the current technology (with 10% tkm coverage). However, Switzerland has a larger potential for electrifying road freight (with 84% tkm coverage) maybe because of applying the smaller gross vehicle weight (GVW) policy for the road transport, having the smaller road network, and covering the larger fast charging service area. Moreover, the best scenario package is selected by considering the CO2 equivalent LCA reduction potential as well as relevant action costs for short and long-term horizons in both countries. In Switzerland, the best scenario package is based on the current technology of battery and fast charging facility which results in 56% CO2 equivalent LCA reduction (0.93 million tons CO2 equivalent per year) with the action cost of -5 €/ton CO2 equivalent. The negative action costs for CO2 equivalent LCA reduction in Switzerland means that the benefits are more than the costs in the relevant electrification scenario. In Finland, the most cost-efficient electrification potential will be achieved in the short-term horizon with the help of 2,348 km electric road systems (ERSs), which results in 24% CO2 equivalent LCA reduction (0.60 million tons CO2 equivalent per year) by the action cost of 550 €/ton CO2 equivalent. However, for the long-term horizon in Finland, 50% increase in the battery capacity, as well as access to ultra-fast charging facilities with 450-kW power, can lead to a better alternative, which results in 35% CO2 equivalent LCA reduction (0.87 million tons CO2 equivalent per year) by the action cost of 522 €/ton CO2 equivalent. The emission and cost results in this thesis consist of high uncertainty ranges because of uncertainty ranges in the emission and cost estimation parameters. The uncertainty ranges would be reduced by using more accurate assumptions based on future research studies. |
Databáze: | OpenAIRE |
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