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The worldwide fast growth of the transportation sector contributes to a large andgrowing share of global greenhouse gas (GHG) emissions. The Swedish TransportAdministration report indicates that emissions from domestic transport increasedin 2018. Having an idea that the workplace parking lots have the potential toincrease the share of renewable energy production in Sweden, an investigation forthe solar-powered electric vehicle (EV) charging station is conducted. This studyaims to clarify the knowledge on what the potentials are, financial assessment, andassessment of the photovoltaic (PV) self-consumption of EV charging in theworkplace charging station. Without knowledge about the highlighted parameters,investors may hesitate to invest in a PV project such as a solar-powered EV chargingstation system. To achieve the objective of this thesis, appropriate tools and/orsoftware are used. PV*SOL software tool is used for simulation and analysis ofenergy system efficiency with EV charging station integrated for different PVsystems deployed in the same location of Sweden. This software tool allows thedesign and calculations of the PV system and EV charging station integrated.Currently registered cars in Sweden indicate that EVs are dominating and will keepthe pulse in the future. This domination will enforce more need for electricity, callfor renewable energy use, and promising significant GHG emissions reduction –sustainable environment. The study has proven that there is no immense insolationin Sweden, thus, the power converter can be undersized up to 30 % with respect tothe PV array to reduced energy loss. A feasible solar-powered EV charging stationrequires several factors such as initial investment (EV charging station, PV module,inverter, transport and installation, operation, and maintenance, etc.), andelectricity trading rate. The study of five PV system cases showed that the increasein size significantly increases the self-sufficiency ratio while self-consumption ratiodecreases. By increasing the PV array, both levelized cost of electricity and paybackperiod were considerably decreased as was intended. However, the more PV arrayincreased in size the more initial investment is required. Study on GHG emissionsof the solar-powered EV charging station as well as the deployment of local energystorage and EV smart charging are recommended as future works.

Energy production is associated with environmental impairment. Most anxious is the greenhouse gas emissions, which also arise from transportation. If battery electric vehicles should be able to alleviate the problem, they must be charged with environmentally friendly produced electricity. This paper investigates a possible relationship between battery electric vehicles and solar photovoltaic panels in household survey data from ENABLE.EU performed in ten European countries autumn 2017 – spring 2018. Estimated with a logit binary choice model, it is found that the probability that a household owns a battery electric vehicle increases if the household owns solar photovoltaic panels. Furthermore, this increase in probability is higher within countries with a higher market diffusion of battery electric vehicle and solar photovoltaic panels (France and the UK). This suggests that policy encouraging home charging of battery electric vehicles from solar photovoltaic panels that includes an energy storage facility could speed up the transition of the vehicle fleet.

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The use of an automatic transmission in pre-transmission parallel hybrid electric vehicles provides greater potential for powertrain optimization than conventional vehicles. By modifying the shift map, the transmission’s gear selection can be adjusted to reduce the energy consumption of the vehicle. A method for determining the optimal shift map for this hybrid vehicle has been implemented using global optimization and software-in-the-loop vehicle simulation. An analysis of the optimization has been performed using software-in-the-loop and hardware-in-the-loop simulation and evaluates two vehicle modes: regenerative braking active and regenerative braking disabled. The results of these two modes illustrate the successful implementation of the global optimization algorithm. However, the evaluation results raise practical concerns about implementing the optimized shift maps in a vehicle and illustrate a problem which must be overcome for future development.

Mississippi State University was selected for participation in the EcoCAR 3 Advance Vehicle Technology Competition. The team designed its architecture around the use of two UQM electric motors, and a Weber MPE 850cc turbocharged engine. To combine the three inputs into a singular output a custom gearbox was designed with seven helical gears. The gears were designed to handle the high torque and speeds the vehicle would experience. The use of this custom gearbox allows for a variety of control strategies. By optimizing the torque supplied by each motor, the overall energy consumption of the vehicle could be reduced. Additionally, studies were completed on the engine to understand the effects of injecting water into the engine’s intake manifold at 25% pedal request from 2000-3500 rpm. Overall, every speed showed an optimum at 20% water to fuel ratio, which obtained reductions in brake specific fuel consumption of up to 9.4%.

This master thesis project aims to complete and verify core functions of a test rig that is designed and built to emulate drive cycles for measuring the energy consumption of HEVs, especially a vehicle named ELBA from KTH Integrated Transport Research Lab (ITRL). To fulfill this goal, a simplified model is created for the test rig, whose involved parameters are identified through various experiments. Then the model is verified by both step voltage responses and sinusoidal current responses. Meanwhile, vehicle dynamics is modeled to calculate required resistance force for road slope emulation. Moreover, an existing method, vehicle equivalent mass, is utilized to compensate dynamic force of the vehicle body, enabling simulation of regenerative braking without an extra flywheel. Together with test rig’s model that is responsible for converting required resistance force to demanded current reference, the rig’s functions are completed and ready for final verification. As a result, the driver of the DC motor on the rig is found to has lower current limitation than required so that the rig is not able to entirely compensate dynamic force of the car. However, the feasibility of the principle is still proved by the tests. Based on the result, recommendations are given to solve the problem and achieve other improvements in the future. / Detta examensarbete syftar till att slutföra och verifiera kärnfunktioner i en testrigg som är designad och byggd för att emulera körcykler för att mäta energiförbrukningen för elhybridbilar, särskilt ett fordon som heter ELBA från KTH Integrated Transport Research Lab (ITRL). För att uppfylla detta mål skapades en förenklad modell för testriggen, vars parametrar identifieras genom olika experiment. Sedan verifieras modellen av både stegspänningssvar och sinusformade strömsvar. Under tiden modelleras fordonsdynamiken för att beräkna erforderlig motståndskraft för väglöpemulering. Samtidigt modelleras fordonsdynamiken för att beräkna den erforderliga motståndskraften för emulering av väglutningar. Dessutom används en befintlig metod, fordonsekvivalentmassa, för att kompensera fordonskroppens dynamiska kraft, vilket möjliggör simulering av regenerativ bromsning utan extra svänghjul. Tillsammans med testriggens modell som är ansvarig för att konvertera erforderlig motståndskraft till efterfrågad strömreferens, är riggens funktioner färdig och redo för slutlig verifiering. Som ett resultat har föraren av likström motorn på riggen visat sig ha lägre strömbegränsning än vad som krävs så att riggen inte helt kan kompensera bilens dynamiska kraft. Emellertid bevisas principens genomförbarhet fortfarande av testerna. Baserat på resultatet ges rekommendationer för att lösa problemet och uppnå andra förbättringar i framtiden.

Electric cars are projected to become the vehicles of the future. A major barrier for their expansion is range anxiety stemming from the limited range a typical EV can travel. EV batteries' performance and capacity are affected by many factors. In particular, the decrease in ambient temperature below a certain threshold will adversely affect the battery's efficiency. This research develops deterministic and two-stage stochastic program model for charging stations' optimal location to facilitate the routing decisions of delivery services that use EVs while considering the variability inherent in climate and customer demand. To evaluate the proposed formulation and solution approach's performance, Fargo city in North Dakota is selected as a tested. For the first chapter, we formulated this problem as a mixed-integer linear programming model that captures the realistic charging behavior of the DCFC's in association with the ambient temperature and their subsequent impact on the EV charging station location and routing decisions. Two innovative heuristics are proposed to solve this challenging model in a realistic test setting, namely, the two-phase Tabu Search-modified Clarke and Wright algorithm and the Sweep-based Iterative Greedy Adaptive Large Neighborhood algorithm. The results clearly indicate that the EV DCFC charging station location decisions are highly sensitive to the ambient temperature, the charging time, and the initial state-of-charge. The results provide numerous managerial insights for decision-makers to efficiently design and manage the DCFC EV logistic network for cities that suffer from high-temperature fluctuations. For the second chapter, a novel solution approach based on the progressive hedging algorithm is presented to solve the resulting mathematical model and to provide high-quality solutions within reasonable running times for problems with many scenarios. We observe that the location-routing decisions are susceptible to the EV logistic's underlying climate, signifying that decision-makers of the DCFC EV logistic network for cities that suffer from high-temperature fluctuations would not overlook the effect of climate to design and manage the respective logistic network efficiently.

Both the solar photovoltaic (PV) installation and electric vehicles (EVs) deployment are increasing significantly in Sweden. With the large-scale integration of PV and EVs, problems such as the voltage deviations and overloading of components can arise, since the existing distribution grids are not designed to host the large shares of new EV loads and the intermittent PV power feed-in. This thesis investigates a C2V2C (i.e., Community to EV to Community) energy flow concept and evaluates how it can improve the power balance performances in communities with both PV and EV integrated in Sweden. Community refers to a group of buildings (i.e., two or more) connected within the same microgrid. It aims to develop a C2V2C model, which utilizes smart charging of electric vehicles to deliver electricity between different communities, for improving the performances at multiple-community-level. A coordinated control of EV smart charging is developed using the genetic algorithm, and its performance is compared with an existing individual control. Two control strategies are considered: (i) minimizing the peak energy exchanges with the grid and (ii) minimizing the electricity costs. Case studies are conducted considering a residential community and workplace community, as well as one EV commuting between them. The study results show that the advanced control achieves a cost reduction of up to 280 % in a summer week compared to the individual control. In a winter week, a performance improvement of up to 13 % can be achieved using advanced control. The advanced control can also reduce the energy exchange peaks with the power grid of the multiple communities. This study has proven the effectiveness of the C2V2C model in enhancing the local power balance at multiple-community-level. It will enhance the resilience and grid-friendliness of building communities, thus paving way for the large PV and EV penetration in the future.

<p>It is widely recognized that a requisite aspect of addressing climate goals is to develop a more sustainable transportation sector. One initiative towards this is the federal administration’s stated goal that 50% of all new vehicle sales will be electric by the year 2030. However, it is a common consensus that this will not occur without significant changes in electric vehicle (EV) adoption trends. In order to meet this goal and significantly diminish transportation greenhouse gas emissions, it is critical to better understand EV adoption at scale. To do this, we must understand at the system level what the progression of adoption will look like and what factors influence that adoption.</p> <p>This problem requires a more granular analysis than has been previously performed. We analyze adoption at the ZIP code level in four US states (CA, CO, NY, WA) with historical data dating to 2011. To understand the progression of adoption, we consider two adoption models (the logistic model and the Bass model) to forecast future EV levels in ZIP codes. We find that the logistic is better for the data that is currently publicly available.</p> <p>We additionally find that EV forecasts must be decomposed into both battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) forecasts. There is sufficient evidence that the adoption processes for these two types of EVs differ.</p> <p>Critically, we extend this analysis to consider the factors influencing adoption. Utilizing the adoption forecasts, we perform spatial regression analyses on the parameters that define the forecast shapes. We examine how multiple sociodemographic, land use, and charging measures correlate with the rate of EV adoption and the lateral shift of early EV adoption.</p> <p>Crucially, we find that multiple measures of charging infrastructure availability correspond with increased adoption; of these, a variation on the distance to fast-charging stations is the most consistent metric across final models. We additionally find that land use type is indeed relevant to adoption. Finally, we are able to corroborate at a granular spatial level numerous sociodemographic variables from the literature.</p> <p>Ultimately, this research can provide valuable insights into adoption trends at a local level and what factors may be best leveraged to promote adoption.</p>