This master’s thesis is about optimal route planning for electric vehicles. The first, theoretical part of this work introduces the issue and describes several heuristic methods that have been used to address optimization tasks. A practical part of the thesis is based around software implementation of the methods described earlier. Finally, several experiments and comparisons of obtained results are carried out.

The road transport sector is heavily dependent on fossil-fuel based technologies, and as a result, contribute a significant share towards climate change and other environmental problems. If the transport sector is to reduce its adverse impacts on climate change, then it requires a global shift towards low-carbon technologies. However, deploying these new technologies brings uncertainties regarding their environmental profile, hence, the need for applying a life cycle approach in evaluating their potential environmental impacts. This thesis aim to evaluate the potential life-cycle environmental impacts associated with travelling 1 km in a battery electric cars (BEV) and plug-in hybrid electric cars (PHEV) operated in the EU at present-day, and in the future up till 2050. The study applied the life cycle assessment (LCA) and ReCiPe Midpoint (H) methodologies to assess and calculate the potential life cycle environmental impacts of all vehicle scenarios. The datasets of the vehicles have been modelled with a modular approach by linking together various vehicle components. The future time perspective based on two future scenarios; the Mod-RES, representing the reference future scenario and the High-RES representing a future ambitious policy scenario.   The EU28 electricity production based on Fichtner, et al. was used to model the use phase all vehicle scenarios. The result showed BEV performed best in indicators for global warming (GWP), ozone depletion and fossil resource scarcity. The thesis best estimate for GWP is 5.61E-2 kgCO2 eq resulting from the BEV_High-RES scenario; representing a decrease in GWP of around 80% and 69% when compared to the ICEV and the baseline BEV respectively. On the other hand, the baseline BEV performed worst in impact categories related to human toxicity and damage to ecosystems; the conventional gasoline car showed the lowest estimate for indicators on human toxicity, acidification and eutrophication as defined in the baseline scenario. Nonetheless, the future scenarios showed promising results for all technologies; as projections for stringent environmental regulations, ‘cleaner’ energy systems and continuous advancement in vehicle technologies offered a significant reduction in all impact categories. Notably, the BEV reduced its impact on toxicity categories to around 38% of the initial values for the baseline scanario. Results are strongly dependent on assumptions regarding the vehicle and battery lifetime, the use phase electricity source and the vehicle consumption.  The findings establish the significance of carrying out a full LCA, including future time perspective and assessing impact categories beyond climate change. Also, it underlined the suggestion that production of electric cars raised more concern for EVs than conventional cars; thus, the tendency for environmental problem-shifting and the need for policy-makers to recognise existing trade-offs. / Vägtransportsektorn är starkt beroende av fossilbränslebaserad teknik och bidrar därmed till en betydande andel av klimatförändringen och andra miljöproblem. Om transportsektorn ska minska dess negativa inverkan på klimatförändringen, krävs det en global övergång till teknik med låga koldioxidutsläpp. Utnyttjandet av denna nya teknik medför dock osäkerhet om sin miljöprofil och därmed behovet av att tillämpa ett livscykelperspektiv vid utvärderingen av deras potentiella miljöpåverkan. Avhandlingen presenterar en livscykelanalys av nuvarande och framtida elfordon. Fokus ligger på batteri elbilar (BEV) och plug-in hybrid elbilar (PHEV) som drivs i EU. EU28-elproduktionen baserad på Fichtner, et al. användes för att beräkna de potentiella livscykelmiljöeffekterna av alla fordonsscenarier baserade på effektkategorier definierade i ReCiPe Midpoint (H) -metoden. Resultatet visade att BEV fungerade bäst i indikatorer för global uppvärmning (GWP), ozonförlust och fossila resurserbrist. Avhandlingens bästa uppskattning för GWP är 5,61E-2 kgCO2 ekv som härrör från BEV_High-RES-scenariot; vilket motsvarar en minskning av GWP på cirka 80% och 69% jämfört med ICEV respektive baseline BEV. Å andra sidan har baslinjens BEV den högsta andelen miljöindikatorer relaterade till human toxicitet och skador på ekosystemen. Den konventionella bensinbilen visade den lägsta uppskattningen av indikatorer för human toxicitet, försurning och eutrofiering enligt definitionen i baslinjen. De framtida scenarierna visade emellertid lovande resultat för all teknik, detta förutsätter strängre miljöregler, "renare" energisystem och kontinuerlig framsteg inom fordonsteknik som kommer att erbjuda en betydande minskning av alla påverkningskategorier. I synnerhet reducerade BEV: s påverkan på toxicitetskategorier till omkring 38% av de ursprungliga värdena för baslinjens scanario. Resultaten är starkt beroende av antaganden om fordonets och batteritiden, användningsfasens elkälla och fordonsförbrukningen. Resultaten visar betydelsen av att utföra en fullständig LCA, inklusive framtida tidsperspektiv och bedömning av påverkningskategorier utanför klimatförändringen. Det understryker också förslaget gällamde produktion av elbilar har en betydande ökade oro för elektriska motorer än konventionella bilar. Det finns en risk för miljöproblemförskjutning och ett behovet av att politiska beslutsfattare erkänner befintliga avvägningar. / REFLEX - Analysis of the European energy system under the aspects of flexibility and technological progress

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

Renewable and green energy development is vigorously supported by most countries to suppress the continuously increasing greenhouse gas (GHG) emissions. However, as the total renewable capacity expands, the growth rate of emissions is not effectively restrained. An unforeseen factor contributing to this growth is the regulation service, which aims to mitigate power frequency deviations caused by the intermittent renewable power generation and unbalanced power supply and demand. Regulation services, normally issued by supply-side balancing authorities, leads to inefficient operations of regulating generators, thus directly contributing to the emissions growth. Therefore, it is urged to find solutions that can stabilize the power frequency with an increased energy using efficiency. Demand response (DR) is an ideal candidate to solve this problem. The current smart grid infrastructure enables a high penetration of smart residential electric loads, including heating, ventilation, and air conditioning systems (HVACs), air conditioners (A/Cs), electric water heaters (EWHs), and plug-in hybrid electric vehicles (PHEVs). Beyond simply drawing power from the grid for local electric demand, those loads can also adjust their power consumption patterns by responding to the control signals sent to them. It has been proved that, if appropriately aggregated and controlled, power consumption of demand-side residential loads possesses a huge potential for providing regulation services. The research of DR is pivotal from the the application perspective due to the efficient usage of renewable energy generation and the high power quality. However, many problems remain open in this area due to the load heterogeneity, device physical constraints, and computational and communication restrictions. In order to move one step further toward industry applications, this PhD thesis is concerned with two cruxes in DR program design: Aggregation Modeling and Control; it deals with two main types of terminal loads: Thermostatically Controlled Appliances (TCAs) (Chapters 2-4) and PHEVs (Chapter 5). This thesis proceeds with Chapter 1 by reviewing the state-of-the-art of DR. Then in Chapter 2, the focus is put on modeling and control of TCAs for secondary frequency control. In order to explicitly describe local TCA dynamics and to provide the aggregator a clear global view, TCAs are aggregated by directly stacking their individual dynamics. Terminal TCAs are assumed in a general case that an arbitrary number of TCAs are equipped with varying frequency drives (VFDs). A centralized model predictive control (MPC) scheme is firstly constructed. In the design, to tackle the TCA lockout effect and to facilitate the MPC scheme, a novel approach for converting time-integrated interdependent logic constraints into inequality constraints are proposed. Since a centralized MPC scheme may introduce non-trivial computational load by using this aggregation model, especially when the number of TCAs increases, a distributed MPC (DMPC) scheme is proposed. This DMPC scheme is validated through a more practical case study that all TCAs are subject to pure ON/OFF control. Chapter 3 targets on aggregation modeling and control of TCAs for the provision of primary frequency control. To efficiently reduce the computational load to facilitate the primary frequency control, the explicit monitoring of terminal TCAs must be compromised. To this end, a 2-D population-based model is proposed, in which TCAs are clustered into state bins according to their temperature information and running status. Within the proposed aggregation framework, individual TCA dynamics' evolutions develop into TCA population migration probabilities, thus the computational load of the centralized controller is dramatically reduced. Based on this model, a centralized MPC scheme is proposed for the primary frequency control. The previously proposed population-based model provides a promising direction for the centralized control. However, in traditional population-based model, TCA lockout effect can only be considered when implementing the control signals. This will cause a mismatch between the nominal control signals and the actually implemented ones. To conquer this, in Chapter 4, an improved population-based model is studied to explicitly formulate the TCA lockout effect in the aggregation model. A DMPC scheme is firstly constructed based on this model. Furthermore, since the predictions of regulation signals may not be available or they may include severe disturbances, a control scheme that does not require future regulation signals is urged. To this end, an optimal control scheme, in which a novel penalty is included to maximize the regulation capability, is proposed to facilitate the most practical scenario. Another type of terminal loads that has a huge potential in providing grid services is PHEV. At this point, Chapter 5 presents the aggregation and charging control of heterogeneous PHEVs for the provision of DR. In contrast to using battery state-of-charge (SOC) solely as the system state, a new aggregation model is proposed by introducing a novel concept, i.e., charging requirement index. This index combines the SOC with drivers' specified charging requirements, thus inherently providing the aggregation model with richer information. A centralized MPC scheme is proposed based on this novel model. Both of the model and controller are validated through an overnight valley-filling case study. Finally, the conclusions of the thesis are summarized and future research topics are presented. / Graduate / 0537 / 0544 / 0548 / mingxiliu419@gmail.com

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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

As the interest for electrified vehicles increases due to a conversion from a fleet of vehicles powered by fossil fuels to more environment friendly and climate neutral options it is important to investigate the different alternatives closely. This thesis analyzes one of the options on a micro level. Data have been collected from a Mitsubishi Outlander plug-in hybrid, including travelled distance, battery state of charge and outdoor temperature. The objective is to develop a model describing how these different factors affect the charging profile. Two models have been developed: one focusing on the total charging time and one for the total electrical charge transferred from the charging station. The analysis show for instance that at higher outside temperatures the charging time decreases but the total charge transferred increases. The final multidimensional models are created by separately looking at one variable at a time to see how it affects the total outcome.

Connected and Autonomous Vehicles (CAVs) benefit from both connectivity between vehicles and city infrastructures and automation of vehicles. In this respect, CAVs can improve safety and reduce traffic congestion and environmental impacts of daily commutes through making collaborative decisions. This dissertation studies how to reduce the energy consumption of vehicles and traffic congestion by making high-level routing decisions of CAVs. The first half of this dissertation considers the problem of eco-routing (finding the energy-optimal route) for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. Several algorithms are proposed that can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. The results show significant energy savings for PHEVs with a near real-time execution time for the algorithms. The second half of this dissertation tackles the problem of routing for fleets of CAVs in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, all CAVs belong to the same fleet and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). It is assumed that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. A framework is proposed that deals with the routing interaction between CAVs and regular uncontrolled vehicles under different penetration rates (fractions) of CAVs. The results suggest collaborative routing decisions of CAVs improve not only the cost of CAVs but also that of the non-CAVs. This framework is further extended to consider congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. A network flow model is devised to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. The results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.

A vehicle fuel’s life does not begin when that fuel is pumped into the tank or the battery is charged. Each kilowatt-hour of fuel that is used has a history traceable back to its original feedstock, be it crude oil, corn, solar energy, or others. In this thesis, a life cycle analysis is performed on E10, E85, B20, hydrogen, and electricity, with the well-to-pump fossil fuel energy use and greenhouse gas emissions compared. Results are presented in the form of either energy or mass per kilowatt of fuel at the plug or at the pump. An analysis of the economic viability of each fuel to the consumer is also demonstrated. E85 is found to have the best well-to-pump fossil fuel energy use at 722 Wh/kWh, while hydrogen demonstrates the best well-to-wheel greenhouse gas emissions with 123 g/km (CO2 equivalent) and electricity produces the lowest vehicle lifetime operating cost of $0.241/mile.

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%.