The feasibility of converting an existing internal combustion powered general aviation aircraft to battery electric propulsion was studied. The theoretical performance of various types of airframes with battery electric propulsion systems was compared to determine which type of airframe would be best suited for conversion. It was found that battery electric propulsion is best used in aircraft intended for slow speed, efficient flight and carrying limited payload which is a mission typically flown in motor gliders. A reference motor glider was selected and a conceptual power system packaging design study was performed. The study determined that a critical component of the power system packaging design was the technical feasibility of packaging the batteries inside of the wing structure. This was driven by center of gravity restrictions. Technical concerns related to a battery-in-wing design were investigated, included wing aeroelastic performance, wing stiffness and wing strength. The results showed that aeroelastic flutter was not a driving design criteria for the reference airframe used as the physical size of the battery did not allow for them to be packaged in wing locations that detrimentally affected flutter performance. The battery packaging layout was instead driven by access for battery maintenance, battery safety and the battery thermal management system. Overall weight change from packaging the batteries in the wing compared to the fuselage was found to be negligible. The resulting aircraft conceptual design indicated a powered flight range with reserves of over 200 miles and a powered flight endurance of greater than 3 hours with 2 persons onboard.

Battery Electric Vehicles and Plug-in Hybrid Vehicles are increasingly becoming more popular in recent years. Stricter regulations from government agencies to curb emissions and reduce impact on climate have led to automobile makers adopt electric powertrains. Eco-Routing is one such method to reduce energy usage in personal transport. EcoRouting is a methodology that determines the route with the least energy consumption between two points. Standard navigation systems often determine the shortest or the fastest route, emphasizing travel time. EcoRouting considers an alternative criterion - energy consumption. In this thesis, an automation methodology is presented that determines the EcoRoute among given route alternatives based on route distance, speed limits, road grades, traffic signs, driver aggression and the powertrain. There are three major objectives in this thesis: Developing the automation methodology for the determination of EcoRoute for use in on-board applications, validating the EcoRouting methodology on actual driving conditions and studying the impact of traffic on the choice of EcoRoute. The automation methodology has been developed on the Android framework for use with on-board applications on Android mobile devices. The automation methodology used to conduct sensitivity studies show that factors such as driver aggression, distance and conditional stops impact energy consumption. The comparison of results of simulation using the automation methodology against results from actual driving to validate the methodology on actual driving conditions show that transient traffic conditions can have significant impact on energy consumption. Finally, route energy consumptions for various traffic conditions are estimated using simulation to understand the impact of traffic on energy consumption and EcoRoute choice. Results that are obtained show that apart from traffic affecting the energy consumption, travel times can have an impact on choice of EcoRoute. / Master of Science / Government agencies have been introducing tighter regulations in order to improve fuel economy and reduce emissions. These regulations are targeted at reducing the impact of vehicle usage on climate. Automobile manufacturers have increasingly adopted electric powertrains to meet these regulations. Battery Electric Vehicles and Plug-in Hybrid Vehicles are more popular than ever. Other methods in reducing environmental impact by automobiles are also being conducted. EcoRouting is one such method. EcoRouting determines the route that consumes the least energy between two locations. EcoRouting requires no modifications to be done on the vehicle or its powertrain. A methodology has been developed in this thesis that takes into account various factors such as traffic signs, speed limits, road grades, powertrain and driver aggression to determine the route that consumes the least energy. Research in this thesis has been divided into three major parts: development of the automation methodology, validating the methodology for actual driving conditions and understanding the impact of traffic on energy consumption. Results of case studies show that the input parameters affect energy consumption significantly. Traveling speeds affect the energy consumption and since transient traffic conditions can affect traveling speeds, transient traffic conditions can have a significant impact on energy consumption. Since energy consumption alone is not considered in determining the EcoRoute and the travel times are also considered so as to not inconvenience the user, traffic conditions impact the choice of EcoRoute both due to differences in energy consumption and travel time.

Battery electric vehicles (BEV) are promoted as a viable near-term technology to reduce the emissions of greenhouse gases (GHG). With Sweden's relatively slow adoption of the BEV in combination with the Swedish government's target of a vehicle fleet independent of fossil fuels by 2030, we study how adoption intentions are influenced by vehicle attribute and when these effects influence BEV adoption. This thesis builds on previous research investigating the effects of barriers and drivers on consumers intentions to adopt electric vehicles. Our study has more specifically examined Swedish consumers intentions to adopt a BEV by conducting a quantitative designed study. We considered the barrier of high perceived price and the driver of environmental self-identity, alongside demographic factors. Furthermore, we also highlight the understudied concept of total cost of ownership (TCO) by studying its moderating role on the relationship between high perceived price, environmental self-identity and consumers intention to adopt BEV’s.  We find that the barrier of high perceived price had no significant influence on intentions to adopt BEV’s, while environmental self-identity was positive and a strong predictor of consumers intentions. Additionally, our results show that the moderating effect of attention to cost (TCOA) and level of information (TCOB) was only significant at one of the four interactions. Concluding that the moderator TCOB has a positive effect on the relationship between high perceived price and intention to adopt BEV’s. These results have implication for BEV marketing, policy and consumers, and suggests that symbolic attributes of the battery electric vehicle have a tendency to reinforce consumers self-identity. This serves as a promising non-financial strategy for increasing BEV adoption. Moreover, the results indicate that consumer with little knowledge of the cost associated with car ownership (low TCOB) are more sensitive to the negative effects from the price of BEV’s in relation to their adoption intentions.

Today, one third of Sweden’s total greenhouse gas emissions come from domestic transport. To reduce these emissions, the Swedish government has set emission targets. To meet these and to avoid the global impacts of climate changes, the need for new technologies and new fuels to replace fossil fuels has become evident. One of the promising solutions to this is battery-electric vehicles.  The following study has been conducted with Scania, a Swedish truck and bus manufacturer that has chosen to invest more in electricity and today offers a heavy battery-electric truck with a range of 250 km. Today, only large companies have invested in heavy electric trucks while small hauliers, who own no more than ten trucks, are not yet sufficiently motivated to participate in the shift to electrification. Since small hauliers account a large share of freight transport, it is of interest to find out what opportunities and barriers they identify with an investment in battery electric trucks in order to include them in the transition. Furthermore, it is of interest to understand which ownership model can manage the barriers and thus facilitate the adaptation for the small hauliers. Therefore, the purpose of this study was formulated as follows:  To identify what small hauliers perceive as opportunities and barriers with the adoption to BEVs and investigate how they can adapt their ownership models to manage the barriers.  To facilitate the answering of the study’s purpose, it was broken down into research questions and associated sub-questions. Literature was used to create categories for opportunities and barriers: Environmental, Economic and Other for opportunities and Range, Total Cost of Ownership (TCO), Charging and Other for barriers. To answer the research questions, empirical data was collected through structured interviews with small hauliers. The data collected was compiled and analyzed to identify opportunities and barriers. The identified barriers were then used as input together with literature to analyze the ownership models owning, leasing and sharing against the most important barriers.  The findings related to the opportunities was that the small hauliers generally did not see many opportunities apart from reduced air pollution. Improved working environment for drivers was another opportunity mentioned. An increasing number of barriers were identified during the interviews. To summarize the barriers, many were linked to the range of a BEV. The small hauliers’ perceptions are that it is a big challenge to replace refueling with charging in their daily operations. This is based on the lack of natural charging opportunities, but also because there is not enough widespread charging infrastructure in the areas where they carry out their journeys in addition to the fact that the charging itself is too time consuming for the hauliers. Several of the barriers were also linked to the new cost structure with high investment costs and unknown residual value.  The most important barriers could be found in all categories. Of the eight most important barriers, four could be managed by ownership models. It was discussed that many barriers cannot be managed using ownership models but must be addressed in other ways. Of the three ownership models examined, leasing and sharing were able to manage the most barriers, with sharing being able to manage all of them to some extent. By joining a trucking center and sharing vehicles, small hauliers gain advantages enjoyed by larger hauliers, which simplifies their ability to participate in the transformation to electricity. The results indicate that the traditional ownership model, where small hauliers own 100% of their trucks, may not be the way forward to participate in the transition to electricity. The purpose could thus be answered by finding that sharing can manage the most important barriers perceived by small hauliers.

In order to meet the worldwide limits on greenhouse gases emissions, a shift from a fossil fuels to a renewable energy-based electric system is required. As this process goes on, both the power generation and consumption profiles are changing in daily pattern and magnitude, so the power grid needs to become more and more flexible in order to handle this variability. At the distribution level, photo-voltaic (PV) systems are, by far, the most widespread distributed energy resource, mostly due to the recent drop in the cost at the residential level. As more and more consumers become also producers (the so called "prosumers") and the volatile solar energy production increases, a higher number of storage systems is required to both avoid grid destabilisation and minimise the CO$_2$ emissions. At the same time, since the transportation sector is responsible for a sizeable part of the total CO$_2$ emissions, electric vehicles (EVs) are bound to replace traditional internal combustion engine vehicles. However, two main issues may arise when a large number of vehicles are connected to the existing power grid at the same time. The first issue is that the electricity required to charge them needs to be renewable, while the second is that, a rapid electrification of the existing vehicles fleet could destabilise the grid. In this context, this thesis aims at partially addressing these two issues by analysing different ways to reduce the impact of both PV systems and EVs on low (LV) and medium (MV) voltage grids. After the introduction and a chapter dealing with the most closely related research work, a novel optimisation algorithm, aimed at obtaining the optimal storage capacity for each prosumer belonging to a "renewable energy community" is presented. The algorithm minimises the dependence of the community on the main grid, which is one of the main purposes of this new model, while minimising the total installed storage capacity. The algorithm is tailored to the specific case study, because it keeps track of the willingness of the users to install a battery and keeps the voltage levels between regulatory limits in the optimisation process. In the second part instead, the effects of "uncontrolled" and "smart" EV-charging the electric vehicles with the aim of reducing the power fluctuations at the MV/LV transformer level are analysed. In particular, the interaction between PV production and EV charging is investigated, while considering the grid voltage fluctuations, the distribution line losses and the transformer loading levels at the same time. The broader impact of smart charging is also analysed by performing a simplified economic and battery wear analysis. Results help in understanding if storage devices can reduce the dependence of a renewable energy community on the main grid, and to what extent it is possible and economically viable to do so. Moreover, results quantify a realistic range of EV and PV system penetration in a LV grid that still allows for a combined minimisation of their impact on the power grid.

This master thesis represents a life cycle impact assessment of a state of the art electrically driven power train. It is expected to be installed in a diesel engine passenger ferry boat, currently transporting passengers in downtown Stockholm archipelago. The assessment has a comparative character in between the currently operating and the new power train in order to differentiate and recognize which of the two propulsion options is the environmentally preferable choice. The scope of the study is directed towards the thorough examination of both power trains so that it can represent most closely the two specific technological cases. Studied and assessed were the three main life cycle phases of each power train – raw materials acquisition and manufacturing, use phase and end of life phase. The fundament of the study involved creating environmental models for each and every component of the drive trains, the propulsion fuel and energy used, and the services related to waste treatment in the last phase of their functional life. The environmental models were later used to build live cycle inventories that served to derive the respectful impact from the item analyzed. The data used to model the battery electric power train was provided directly from the manufacturer, where the end of life procedures carried out were assumed where possible. The main battery pack for the electric power train was not modeled in terms of  end of life procedures due to insufficiency of information. Almost no generic information was available to model the diesel engine and it was calculated by creating auxiliary simplified cad models. The rest of the data required to achieve an environmental inventory regarding the power train was available from a subcontractor. Both studied options were modeled with allocation approach that includes the avoided production of materials at the waste treatment stage where there was sufficient information to do that. There was none to model the main battery packs avoided production which is a major component of the battery electric system. To model the use phase of the diesel engine power train, research data regarding combustion emissions and waterborne emissions was utilized. A number of electricity mix models were applied to create a sensitivity analysis of the operation phase of the battery electric power train. Chosen for baseline scenarios simulating the use phases of both power trains are use of Nordel market electricity mix and the combustion of low sulfur diesel with five volumetric percent rape methyl ester additive. For the purposes of the assessment eighteen midpoint impact indicators were used to cover the areas of global warming potential, human health and quality of eco systems. The results from the study show that the estimated impact from both power trains is small enough to have almost no influence on the results from the two baseline scenarios. Based on this it was concluded that for future research of similar cases either generic information can be used or a cut-off can be applied. After the assessment, more environmentally favorable was estimated the diesel engine power train because of the large burdens from the battery manufacturing in the battery electric option. Further assessment determined that the diesel engine power train again is less environmentally intensive than the battery electric with the main battery burdens excluded. In the overall life cycle impact assessment both power train showed different results in the different impact categories, which could not place a definitive propulsion option of choice. The conclusions from the analysis are that the diesel engine power train causes higher impact in the categories related to global warming, fossil depletion and in most ecosystems quality indicators. The battery electric version in its base line scenario, on the other hand, expresses higher impact in categories related to human health and in the remaining eco system quality midpoint-scores.

In this thesis, we propose a stochastic power management strategy for in-wheel motor electric vehicles (IWM-EVs) to optimize energy consumption and to increase driving range. The driving range for EVs is a critical issue since the battery is the only source of energy. Considering the unpredictable nature of the driver’s power demand, a stochastic dynamic programing (SDP) control scheme is employed. The Policy Iteration Algorithm, one of the efficient SDP algorithms for infinite horizon problems, is used to calculate the optimal policies which are time-invariant and can be implemented directly in real-time application. Applying this control package to a high-fidelity model of an in-wheel motor electric vehicle developed in the Autonomie/Simulink environment results in considerable battery charge economy performance, while it is completely free to launch since it does not need further sensor and communication system. In addition, a skid avoidance algorithm is integrated to the power management strategy to maintain the wheels’ slip ratios within the desired values. Undesirable slip ratio causes poor brake and traction control performances and therefore should be avoided. The simulation results with the integrated power management and skid avoidance systems show that this system improves the braking performance while maintaining the power efficiency of the power management system.

Due to an increasing concern of the introduction of greenhouse gas (GHG) regulations in many jurisdictions, the underground mining industry is in high demand to tackle climate change through innovative measures. In order to stay competitive, cope with rising energy costs and GHG regulations, mining companies will have to consider the alternative to go fully electric. As underground mines progress through time they are becoming deeper and deeper, resulting in longer haulage distances and thus an increasing energy demand. The research in this thesis was conducted to analyze and develop a simulation tool to investigate the replacement of conventional diesel haulage trucks with battery electric trucks that include a fast-charging capability in an underground mine environment. The results show that there is a major difference in the achievable production rates depending on the mine topography and a need for opportunity charging. Furthermore, the developed tool could aid in decision making and provide a good frame of reference of the feasibility of replacing an existing diesel operation by a battery electric one.

Background:    Climate change has been becoming a major topic of interest, for research as well as for society. Transport caused emissions are constantly growing which forced the European Union to set the goal to decrease transport related emissions by 60% until 2050. A heavily discussed and promising tool seems to be being found in battery electric (BEV) vehicle adoption. However, BEV adoption seems to be underachieved which raises questions about potential influencing factors on BEV adoption. Additionally, latest research elaborated perception to be one of the key topics of interest for consumers adopting fully electric vehicles. Purpose:           The purpose of this thesis was to examine influencing factors affecting BEV adoption and the perception of those factors among generation Y consumers.  Method:            To attain the purpose, a qualitative research was conducted. After collecting secondary data to evaluate existing factors influencing consumer’s willingness to adopt BEVs, 16 participants accountable to generation Y have been interviewed using semi-structured interviews. Using a qualitative research approach valuable data and in-depth insights which are essential for markets such as the automotive industry. Conclusion:      The results show that there is a generally positive attitude towards BEV adoption among generation Y. However, there have been five influencing factors affecting consumer’s willingness to adopt BEVs. Analysis of the perception of each factor allowed the research team to get in-depth insights and to elaborate the importance of each factor and how the factors interrelate. Based on the gathered data relationships between influencing factors have been highlighted and based on TAM and TRI models a new model for further research has been developed.

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