Since the transportation sector still relies mostly on fossil fuels, the emissions and overall environmental impacts of the transportation sector are particularly relevant to the mitigation of the adverse effects of climate change. Sustainable transportation therefore plays a vital role in the ongoing discussion on how to promote energy insecurity and address future energy requirements. One of the most promising ways to increase energy security and reduce emissions from the transportation sector is to support alternative fuel technologies, including electric vehicles (EVs). As vehicles become electrified, the transportation fleet will rely on the electric grid as well as traditional transportation fuels for energy. The life cycle cost and environmental impacts of EVs are still very uncertain, but are nonetheless extremely important for making policy decisions. Moreover, the use of EVs will help to diversify the fuel mix and thereby reduce dependence on petroleum. In this respect, the United States has set a goal of a 20% share of EVs on U.S. roadways by 2030. However, there is also a considerable amount of uncertainty in the market share of EVs that must be taken into account. This dissertation aims to address these inherent uncertainties by presenting two new models: the Electric Vehicles Regional Optimizer (EVRO), and Electric Vehicle Regional Market Penetration (EVReMP). Using these two models, decision makers can predict the optimal combination of drivetrains and the market penetration of the EVs in different regions of the United States for the year 2030. First, the life cycle cost and life cycle environmental emissions of internal combustion engine vehicles, gasoline hybrid electric vehicles, and three different EV types (gasoline plug-in hybrid EVs, gasoline extended-range EVs, and all-electric EVs) are evaluated with their inherent uncertainties duly considered. Then, the environmental damage costs and water footprints of the studied drivetrains are estimated. Additionally, using an Exploratory Modeling and Analysis method, the uncertainties related to the life cycle costs, environmental damage costs, and water footprints of the studied vehicle types are modeled for different U.S. electricity grid regions. Next, an optimization model is used in conjunction with this Exploratory Modeling and Analysis method to find the ideal combination of different vehicle types in each U.S. region for the year 2030. Finally, an agent-based model is developed to identify the optimal market shares of the studied vehicles in each of 22 electric regions in the United States. The findings of this research will help policy makers and transportation planners to prepare our nation*s transportation system for the future influx of EVs. The findings of this research indicate that the decision maker*s point of view plays a vital role in selecting the optimal fleet array. While internal combustion engine vehicles have the lowest life cycle cost, the highest environmental damage cost, and a relatively low water footprint, they will not be a good choice in the future. On the other hand, although all-electric vehicles have a relatively low life cycle cost and the lowest environmental damage cost of the evaluated vehicle options, they also have the highest water footprint, so relying solely on all-electric vehicles is not an ideal choice either. Rather, the best fleet mix in 2030 will be an electrified fleet that relies on both electricity and gasoline. From the agent-based model results, a deviation is evident between the ideal fleet mix and that resulting from consumer behavior, in which EV shares increase dramatically by the year 2030 but only dominate 30 percent of the market. Therefore, government subsidies and the word-of-mouth effect will play a vital role in the future adoption of EVs.

Battery electric trucks (BETs) offer environmental benefits in terms of reduced carbon emissions and enhanced energy efficiency but have been challenged with economic viability compared to conventional internal combustion engine trucks (ICETs) caused by substantial acquisition costs, limited charging infrastructure, and concerns regarding range and payload capacity.  Previous studies focus on TCO at the vehicle or policy level but overlook the system and firm-level impacts. Operational aspects like vehicle utilization, battery utilization, charging planning, and route optimization are often ignored, potentially underestimating electric freight cost-competitiveness.The research gap does not address the practical needs of fleet operators, especially in scenarios where charging infrastructure is lacking. There is therefore a need to consider the complex system level interactions, market dynamics, technology developments, and operational processes involved in freight shipping. By applying a decision-making under deep uncertainty (DMDU) framework, this study enables informed decisions in unpredictable scenarios, bridging the gap between strategic choices like battery capacity and operational optimization like route planning. This study identifies the most significant factors that affect the TCO of BET fleets and cost-competitiveness relative to ICET fleets, taking into account market-operational interfaces between unpredictable market dynamics and operational processes such as stochastic demand and feature selection from a strategic and operational perspective. 40 tonne truck-trailers for freight distribution networks with distances up to 250 km are considered in the study.  A TCO model of BET and ICET fleets was developed taking into account vehicle route optimization, vehicle selection, and vehicle utilization which was then programmatically iterated by sampling and simulating optimized vehicle routes for a total of 220 224 iterations. The parameter space was screened and reduced with Feature Scoring using Extra Trees approximation of 1st order Sobol Indices. The reduced parameter space was then sampled using Sobol sampling to conduct a Sobol Global Variance decomposition Analysis of TCO, TCO delta, and service level in order to identify the most significant factors affecting BET fleet TCO and cost-competitiveness.To identify cost-competitive scenarios, the Patient Rule Induction Method (PRIM) was used to identify parameter sub spaces to determine scenarios where BET fleets have a lower TCO than ICET fleets. Further visual analysis was done using linear and polynomial regression and kernel density estimation. The analysis shows that both TCO and cost-competitiveness of BETs are primarily affected by shipment demand, distance between distribution center and delivery sites, and battery size, and that a trade-off is made between cost-competitiveness and service level. The results show that cost-competitiveness of electric freight scales with demand, with larger fleets being better able to optimize routing and shipment allocation; balancing the shipment demand to minimize charging times that otherwise would make the fleet less competitive than their fossil-fuel counterparts. This, paired together with higher degrees of vehicle utilization and appropriate battery sizing, allow for electric freight to be cost-competitive even for long-haul distances up to 250 km.  Furthermore, optimization of the Electric Vehicle Routing Problem (E-VRP) with shifts and time windows is shown to have a highly significant effect when minimizing TCO on a fleet level, with the vast majority of optimal ICET routes not being optimal for BETs.The benefits of E-VRP optimization scales with demand and fleet size, indicating that large-scale electrification is required to make BETs cost-competitive.Electrification of road freight is therefore highly contingent on effective route planning and charging scheduling with E-VRP optimization in order to be cost-competitive, which has not been considered in previous literature. Thus previous literature have therefore likely underestimated the cost-competitiveness of electric freight, particularly at medium-long haul distances. / Battery electric trucks (BETs), även kända som batterielektriska lastbilar, erbjuder miljömässiga fördelar genom minskade koldioxidutsläpp och förbättrad energieffektivitet. Men de har utmanats när det kommer till ekonomisk konkurrenskraft jämfört med konventionella lastbilar med förbränningsmotor (ICETs) på grund av höga inköpskostnader, begränsad laddinfrastruktur och oro över räckvidd och lastkapacitet. Tidigare studier har fokuserat på TCO (totala ägandekostnader) på fordon- eller policynivå men har inte betraktat TCO på nätverksnivå och från det enskilda företagets perspektiv. Operativa aspekter som fordonssutnyttjande, batteriutnyttjande, laddningsplanering och ruttoptimisering ignoreras ofta, vilket potentiellt leder till en underskattning av elektrisk frakts kostnadskonkurrenskraft. Forskningsluckan tar inte upp de praktiska behoven hos fordonsflottoperatörer, särskilt i scenarier där laddinfrastrukturen är bristfällig. Det finns därför ett behov av att granska komplexa systemnivåinteraktioner, marknadens dynamik, teknikutveckling och operativa processer som är involverade i godstransport. Genom att tillämpa \textit{decision-making under deep uncertainty} (DMDU) möjliggör denna studie informerade beslut i scenarier präglade av osäkerhet och studerar interaktionseffecter mellan strategiska val som batterikapacitet och operativ optimering som t.ex.\ ruttplanering. Denna studie identifierar de mest betydande faktorer som påverkar TCO för BET-flottor och deras kostnadskonkurrenskraft jämfört med ICET-flottor, med beaktande av gränssnitten mellan marknadsdynamik och operativa processer såsom stokastisk efterfrågan och urval av funktioner ur såväl strategisk som operativ synvinkel. 40-ton lastbilssläp för nätverk med avstånd upp till 250 km beaktas inom omfånget för studien. En TCO-modell för BET- och ICET-flottor utvecklades med hänsyn till ruttoptimering, fordonsval och fordonsutnyttjande, vilket sedan programmässigt itererades genom provtagning och simulering av optimerade fordonsrutter för sammanlagt 220 224 iterationer. Parameterrummet granskades och minskades med hjälp av funktionsskattning med hjälp av Extra Trees-approximation av Sobol-indices av första ordningen. Det reducerade parameterrummet provtogs sedan med Sobol-provtagningsmetod för att genomföra en global variansdekomponering av TCO, TCO-delta och servicenivå för att identifiera de mest betydande faktorerna som påverkar BET-flottans TCO och kostnadskonkurrenskraft. För att identifiera kostnadskonkurrenskraftiga scenarier användes Patient Rule Induction Method (PRIM) för att identifiera parametrarum som visar scenarier där BET-flottor har lägre TCO än ICET-flottor. Vidare utfördes visuell analys med linjär och polynomisk regression samt kärnskattning. Analysen visar at kostnadskonkurrenskraft för tunga elektriska fordon primärt påverkas av efterfrågan, köravstånder och batteristorlek, och att det görs en avvägning mellan kostnadskonkurrenskraft och servicenivå. Resultaten visar at kostnadskonkurrenskraft ökar i takt med efterfrågan, då större flottor kan mer fördelaktigt optimera rutter och allokering av leveranser till varje fordon genom att transportefterfrågan balanseras sådan att tiden för laddning minimeras, vilket hade annars gjort de elektriska flottorna mindre konkurrenskraftiga gentemot fossildrivna flottor av tunga fordon. Detta i samband med högre utnyttjandegrad av fordonen och val av rätt batteristorlek gjör att elektrisk godstransport kan vara kostnadskonkurrenskraftig även vid längre körsträckor upp till 250 km. Vidare visar ruttoptimering för BETs (E-VRP) sig vara av stor betydelse när det gäller att minimera TCO på flottnivå, medan majoriteten av optimala ICET-rutter inte är optimala för BETs.Fördelarna med E-VRP optimering skalar med ökande efterfrågan och flottstorlek, vilket tyder på att storskalig elektrifiering behövs för att göra BETs kostnadskonkurrenskraftigaElektrifiering av godstransport är därför starkt beroende av effektiv rutt- och laddningsplanering med E-VRP-optimering. Tidigare litteratur har sannolikt underskattat kostnadskonkurrenskraften för elektrisk godstransport, särskilt vid medellånga och långa transportavstånd.