Temperature is one of the most important parameters for the performance, safety, and aging of lithium-ion batteries and has been linked to all main barriers for widespread commercial success of electric vehicles. The aim of this thesis is to highlight the importance of temperature effects, as well as to provide engineering tools to study these. The mass transport phenomena of the electrolyte with LiPF6  in EC:DEC was fully characterized in between 10 and 40 °C and 0.5 and 1.5 M, and all mass transport properties were found to vary strongly with temperature. A superconcentrated electrolyte with LiTFSI in ACN was also fully characterized at 25 °C, and was found to have very different properties and interactions compared to LiPF6  in EC:DEC. The benefit of using the benchmarking method termed electrolyte masstransport resistivity (EMTR) compared to using only ionic conductivity was illustrated for several systems, including organic liquids, ionic liquids, solid polymers, gelled polymers, and electrolytes containing flame-retardant additives. TPP, a flame-retardant electrolyte additive, was evaluated using a HEV load cycle and was found to be unsuitable for high-power applications such as HEVs. A large-format commercial battery cell with a thermal management system was characterized using both experiments and a coupled electrochemical and thermal model during a PHEV load cycle. Different thermal management strategies were evaluated using the model, but were found to have only minor effects since the limitations lie in the heat transfer of the jellyroll. / Temperatur är en av de viktigaste parametrarna gällande ett litiumjonbatteris prestanda, säkerhet och åldring och har länkats till de främsta barriärerna för en storskalig kommersiell framgång för elbilar. Syftet med den här avhandlingen är att belysa vikten av temperatureffekter, samt att bidra med ingenjörsverktyg att studera dessa. Masstransporten för elektrolyten LiPF6  i EC:DEC karakteriserades fullständigt i temperaturintervallet 10 till 40 °C för LiPF6-koncentrationer på 0.5 till 1.5 M. Alla masstransport-egenskaper fanns variera kraftigt med temperaturen. Den superkoncentrerade elektrolyten med LiTFSI i ACN karakteriserades även den fullständigt vid 25 °C. Dess egenskaper och interaktioner fanns vara väldigt annorlunda jämfört med LiPF6  i EC:DEC. Fördelen med att använda utvärderingsmetoden elektrolytmasstransportresistivitet (EMTR) jämfört med att endast mäta konduktivitet illustrerades för flertalet system, däribland organiska vätskor, jonvätskor, fasta polymerer, gellade polymerer, och elektrolyter med flamskyddsadditiv. Flamskyddsadditivet TPP utvärderades med en hybridbils-lastcykel och fanns vara olämplig för högeffektsapplikationer, som hybridbilar. Ett kommersiellt storformatsbatteri med ett temperatur-kontrollsystem karakteriserades med b.de experiment och en kopplad termisk och elektrokemisk modell under en lastcykel utvecklad för plug-inhybridbilar. Olika strategier för kontroll av temperaturen utvärderades, men fanns bara ha liten inverkan på batteriets temperatur då begränsningarna för värmetransport ligger i elektrodrullen, och inte i batteriets metalliska ytterhölje. / <p>QC 20150522</p> / Swedish Hybrid Vehicle Center

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.

To promote higher air quality and reduce greenhouse gas emissions, the Californian government is investing heavily in developing public charging infrastructure to meet its electric vehicle adoption goal of five million zero-emission vehicles on the road by 2030. This thesis investigates the effect of public charging infrastructure availability on electric vehicle adoption at the zip code level in California. The analysis considers other factors that may influence electric vehicle adoption such as education level, income, commute time, gas prices, and public transportation rate. The findings suggest that public charging infrastructure availability does significantly positively correlate with electric vehicle registrations. Linear regressions were run using data from the U.S Department of Energy Alternative Fuels Data Center, IHS Markit vehicle registration data, and the US Census Bureau. The findings support continued investment in public charging infrastructure as a means of promoting electric vehicle adoption.

This thesis evaluates emerging electric vehicle technology and estimates what effect it might have on how an oil company decides on its gas station network. It is conducted using data from South Korea, a country poised for a fast adoption of electric vehicles. The study first reviews the literature to gather reasonable cases of electric vehicle penetration. Also, after researching technology-diffusion theories, the study selects a model that can well explain the literature review data. The scenarios induced by this function are utilized as the main uncertainties confronting an oil company’s network decision model. Based on a probabilistic simulation, the study finds that the effects of technology diffusion alter the priority order of an oil company’s network decision alternatives. Namely, after the overall uncertainty level rises, directly owning gas station, with its heavy initial investment, is not preferred for an oil company’s network strategy. From the result, the study also estimates the scale of the new technology’s effect. Such effect is found to be significant enough to alter a part of an oil company’s retail strategy. Nevertheless, such effect cannot be shown to be so great as to change the current retail oil market structures. / text

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.

abstract: This study evaluates the potential profitability and environmental benefit available by providing renewable energy from solar- or wind-generated sources to electric vehicle drivers at public charging stations, also known as electric vehicle service equipment (EVSE), in the U.S. Past studies have shown above-average interest in renewable energy by drivers of plug-in electric vehicles (PEVs), though no study has evaluated the profitability and environmental benefit of selling renewable energy to PEV drivers at public EVSE. Through an online survey of 203 U.S.-wide PEV owners and lessees, information was collected on (1) current PEV and EVSE usage, (2) potential willingness to pay (WTP) for upgrading their charge event to renewable energy, and (3) usage of public EVSE if renewable energy was offered. The choice experiment survey method was used to avoid bias known to occur when directly asking for WTP. Sixty percent of the participants purchased their PEVs due to environmental concerns. The survey results indicate a 506% increase in the usage of public pay-per-use EVSE if renewable energy was offered and a mean WTP to upgrade to renewable energy of $0.61 per hour for alternating current (AC) Level 2 EVSE and $1.82 for Direct Current (DC) Fast Chargers (DCFC). Based on data from the 2013 second quarter (2Q) report of The EV Project, which uses the Blink public EVSE network, this usage translates directly to an annual gross income increase of 668% from the original $1.45 million to $11.1 million. Blink would see an annual cost of $16,005 per year for the acquisition of the required renewable energy as renewable energy credits (RECs). Excluding any profit seen purely from the raise in usage, $3.8 million in profits would be gained directly from the sale of renewable energy. Relative to a gasoline-powered internal combustion engine passenger vehicle, greenhouse gas (GHG) emissions are 42% less for the U.S. average blend grid electricity-powered electric vehicle and 99.997% less when wind energy is used. Powering all Blink network charge events with wind energy would reduce the annualized 2Q 2013 GHG emissions of 1,589 metric tons CO2 / yr to 125 kg CO2 / yr, which is the equivalent of removing 334 average U.S. gasoline passenger cars from the road. At the increased usage, 8,031 metric tons CO2 / yr would be prevented per year or the equivalent of the elimination of 1,691 average U.S. passenger cars. These economic and environmental benefits will increase as PEV ownership increases over time. / Dissertation/Thesis / Masters Thesis Technology 2014

Electric vehicles (EV) have become very popular in recent years because they are a more sustainable, efficient, and environmentally friendly transportation option than traditional fossil-fuel vehicles. Increased EV charging can cause overheating, accelerated aging, and eventual early failure of the distribution transformers, as the distribution networks have not been established foreseeing a large number of EVs as loads. This thesis makes contributions in two main areas to help reduce the accelerated aging of distribution transformers as the number of EVs on the road continues to rise. Firstly, vehicle smart charging is investigated to spread out the EV charging loads and hence decrease transformer heating and aging. Most EV smart charging algorithms require the use of extensive and costly infrastructure, including sensors, communication networks, controllable chargers, and central smart agents. This thesis proposes a new vehicle-directed smart charging strategy, called Random-In-Window (RIW) which allows individual vehicles to spread out their charging without any costly additional infrastructure. Detailed simulation results prove the advantages of this proposed algorithm. Secondly, to further reduce EV charging loads on the grid, a large-scale solar-charged electric vehicle (SEV) is proposed. While RIW smart charging has only grid benefits, SEVs can contribute to grid benefit, driver benefit, and environmental benefit, as shown through detailed simulation results, making it a viable solution to transformer aging mitigation. To turn the SEV concept into reality, this research also proposes a fast maximum power point tracking algorithm for partially shaded conditions, and an algorithm which optimizes photovoltaic (PV) cell size and arrangement along with the power electronic converter design for on-board solar charging. Thus, the proposed solutions in this research can help reduce distribution transformer aging as EV penetrations continue to rise and increase the environmental benefits of EVs through optimized solar charging. / Thesis / Doctor of Philosophy (PhD) / Overheating, accelerated aging, and eventual early failure of the distribution transformers caused by EV charging stress is a pressing concern that needs to be addressed. This thesis proposes two new vehicle-directed smart charging strategies and a concept of solar-charged electric vehicle (SEV) to help reduce the accelerated aging of distribution transformers. System level analysis of the mitigation of transformer aging using these two approaches with added driver and environmental benefits warrants the manufacturing and design challenges of the SEVs. Thus, this thesis proposes a fast and novel global maximum power point tracking algorithm well suited to fast moving vehicles for maximum solar power extraction at all times, especially during partial shading conditions, and an optimization process of the on-board PV cell dimension and number of such cells in series and parallel in the array based on power electronic converter for higher efficiency, lower cost, and lower mass.

No description available.

The market segment of hybrid-electric and full function electric vehicles is growing within the automotive transportation sector. While many papers exist concerning fuel economy or fuel consumption and the limitations of conventional powertrains, little published work is available for vehicles which use grid electricity as an energy source for propulsion. Generally, the emphasis is put solely on the average drive cycle efficiency for the vehicle with very little thought given to propelling and braking powertrain losses for individual components. The modeling section of this paper will take basic energy loss equations for vehicle speed and acceleration, along with component efficiency information to predict the grid energy consumption in AC Wh/km for a given drive cycle. This paper explains how to calculate the forces experienced by a vehicle while completing a drive cycle in three different ways: using vehicle characteristics, United States Environmental Protection Agency's (EPA) Dynamometer "target" coefficients, and an adaptation of the Sovran parameters. Once the vehicle forces are determined, power and energy demands at the wheels are determined. The vehicle power demands are split into propelling, braking, and idle to aide in the understanding of what it takes to move a vehicle and to identify possible areas for improvement. Then, using component efficiency data for various parameters of interest, the energy consumption of the vehicle as a pure EV is supplied in both DC (at the battery terminals) and AC (from the electric grid) Wh/km. The energy that flows into and out of each component while the vehicle is driving along with the losses at each step along the way of the energy path are detailed and explained. The final goal is to make the results of the model match the vehicle for any driving schedule. Validation work is performed in order to take the model estimates for efficiencies and correlate them against real world data. By using the Virginia Tech Range Extended Crossover (VTREX) and collecting data from testing, the parameters that the model is based on will be correlated with real world test data. The paper presents a propelling, braking, and net energy weighted drive cycle averaged efficiency that can be used to calculate the losses for a given cycle. In understanding the losses at each component, not just the individual efficiency, areas for future vehicle improvement can be identified to reduce petroleum energy use and greenhouse gases. The electric range of the vehicle factors heavily into the Utility Weighted fuel economy of a plug-in hybrid electric vehicle, which will also be addressed. / Master of Science

Smart Grid (SG) is an electrical grid enhanced with information and communication technology capabilities, so it can support two-way electricity and communication flows among various entities in the grid. The aim of SG is to make the electricity industry operate more efficiently and to provide electricity in a more secure, reliable and sustainable manner. Automated Meter Reading (AMR) and Smart Electric Vehicle (SEV) charging are two SG applications tipped to play a major role in achieving this aim. The AMR application allows different SG entities to collect users’ fine-grained metering data measured by users’ Smart Meters (SMs). The SEV charging application allows EVs’ charging parameters to be changed depending on the grid’s state in return for incentives for the EV owners. However, both applications impose risks on users’ privacy. Entities having access to users’ fine-grained metering data may use such data to infer individual users’ personal habits. In addition, users’ private information such as users’/EVs’ identities and charging locations could be exposed when EVs are charged. Entities may use such information to learn users’ whereabouts, thus breach their privacy. This thesis proposes secure and user privacy-preserving protocols to support AMR and SEV charging in an efficient, scalable and cost-effective manner. First, it investigates both applications. For AMR, (1) it specifies an extensive set of functional requirements taking into account the way liberalised electricity markets work and the interests of all SG entities, (2) it performs a comprehensive threat analysis, based on which, (3) it specifies security and privacy requirements, and (4) it proposes to divide users’ data into two types: operational data (used for grid management) and accountable data (used for billing). For SEV charging, (1) it specifies two modes of charging: price-driven mode and price-control-driven mode, and (2) it analyses two use-cases: price-driven roaming SEV charging at home location and price-control-driven roaming SEV charging at home location, by performing threat analysis and specifying sets of functional, security and privacy requirements for each of the two cases. Second, it proposes a novel Decentralized, Efficient, Privacy-preserving and Selective Aggregation (DEP2SA) protocol to allow SG entities to collect users’ fine-grained operational metering data while preserving users’ privacy. DEP2SA uses the homomorphic Paillier cryptosystem to ensure the confidentiality of the metering data during their transit and data aggregation process. To preserve users’ privacy with minimum performance penalty, users’ metering data are classified and aggregated accordingly by their respective local gateways based on the users’ locations and their contracted suppliers. In this way, authorised SG entities can only receive the aggregated data of users they have contracts with. DEP2SA has been analysed in terms of security, computational and communication overheads, and the results show that it is more secure, efficient and scalable as compared with related work. Third, it proposes a novel suite of five protocols to allow (1) suppliers to collect users accountable metering data, and (2) users (i) to access, manage and control their own metering data and (ii) to switch between electricity tariffs and suppliers, in an efficient and scalable manner. The main ideas are: (i) each SM to have a register, named accounting register, dedicated only for storing the user’s accountable data, (ii) this register is updated by design at a low frequency, (iii) the user’s supplier has unlimited access to this register, and (iv) the user cancustomise how often this register is updated with new data. The suite has been analysed in terms of security, computational and communication overheads. Fourth, it proposes a novel protocol, known as Roaming Electric Vehicle Charging and Billing, an Anonymous Multi-User (REVCBAMU) protocol, to support the priced-driven roaming SEV charging at home location. During a charging session, a roaming EV user uses a pseudonym of the EV (known only to the user’s contracted supplier) which is anonymously signed by the user’s private key. This protocol protects the user’s identity privacy from other suppliers as well as the user’s privacy of location from its own supplier. Further, it allows the user’s contracted supplier to authenticate the EV and the user. Using two-factor authentication approach a multi-user EV charging is supported and different legitimate EV users (e.g., family members) can be held accountable for their charging sessions. With each charging session, the EV uses a different pseudonym which prevents adversaries from linking the different charging sessions of the same EV. On an application level, REVCBAMU supports fair user billing, i.e., each user pays only for his/her own energy consumption, and an open EV marketplace in which EV users can safely choose among different remote host suppliers. The protocol has been analysed in terms of security and computational overheads.