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.

This work presents novel modeling and experimental techniques that provide insight into liquid-phase mass transport and electron transfer processes in lithium-ion batteries. These included liquid-phase ionic mass transport (conduction and diffusion), lithium diffuion in the solid phase and electronic transport in the solid phase. Fundamental understanding of these processes is necessary to efficiently design and optimize lithium-ion batteries for different applications. To understand the effect of electrode structure on the electronic resistance of the cathode, we tested power performance of cathodes with combinations of three different carbon conductivity additives: vapor-grown carbon fibers (CF), carbon black (CB) and graphite (GR). With all other factors held constant, cathodes with a mixture of CF+CB were found to have the best power-performance, followed by cells containing CF only and then by CB+GR. Thus, the use of carbon fibers as conductive additive was found to improve the power performance of cells compared to the baseline (CB+GR). The enhanced electrode performance due to the fibers also allows an increase in energy density while still meeting power goals. About one-third of the available energy was lost to irreversible processes when cells were pulse-charged or discharged at the maximum rate allowed by voltage-cutoff constraints. We developed modeling and experimental techniques to quantify tortuosity in electrolyte-filled porous battery structures (separator and active-material film). Tortuosities of separators were measured by two methods, AC impedance and polarization-interrupt, which produced consistent results. The polarization-interrupt experiment was used similarly to measure effective electrolyte transport in porous films of cathode materials, particularly films containing lithium iron phosphate. An empirical relationship between porosity and the tortuosity of the porous structures was developed. Our results demonstrate that the tortuosity-dependent mass transport resistance in porous separators and electrodes is significantly higher than that predicted by the oft-used Bruggeman relationship. To understand the dominant resistances in a lithium battery, we developed and validated a model for lithium iron phosphate cathode. In doing so we considered unique physical features of this active material. Our model is unusual in terms of the range of experimental conditions for which it is validated. Various submodel and experimental techniques were used to find physically realistic parameters. The model was tested with different discharge rates and thicknesses of cathodes, in all cases showing good agreement, which suggests that the model takes into account physical realities with different thicknesses. The model was then used to find the dominant resistance for the tested cathodes. The model suggests that the inter-particle contact resistance between carbon and the active-material particles was a dominant resistance for the tested cathodes.

In today’s world of volatile fuel prices and climate concerns, there is little study on the relation between vehicle ownership patterns and attitudes toward potential policies and vehicle technologies. This work provides new data on ownership decisions and owner preferences under various scenarios, coupled with calibrated models to microsimulate Austin’s household-fleet evolution. Results suggest that most Austinites (63%, population-corrected share) support a feebate policy to favor more fuel efficient vehicles. Top purchase criteria are vehicle purchase price, type/class, and fuel economy (with 30%, 21% and 19% of respondents placing these in their top three). Most (56%) respondents also indicated that they would seriously consider purchasing a Plug-In Hybrid Electric Vehicle (PHEV) if it were to cost $6,000 more than its conventional, gasoline-powered counterpart. And many respond strongly to signals on the external (health and climate) costs of a vehicle’s emissions, more strongly than they respond to information on fuel cost savings. 25-year simulations suggest that 19% of Austin’s vehicle fleet could be comprised of Hybrid Electric Vehicles (HEVs) and PHEVs under adoption of a feebate policy (along with PHEV availability in Year 1 of the simulation, and current gas prices throughout). Under all scenarios vehicle usage levels (in total vehicle miles traveled [VMT]) are predicted to increase overall, along with average vehicle ownership levels (per household, and per capita); and a feebate policy is predicted to raise total regional VMT slightly (just 4.43 percent, by simulation year 25), relative to the trend scenario, while reducing CO2 emissions only slightly (by 3.8 percent, relative to trend). Doubling the trend-case gas price to $5/gallon is simulated to reduce the year-25 vehicle use levels by 17% and CO2 emissions by 22% (relative to trend). Two- and three-vehicle households are simulated to be the highest adopters of HEVs and PHEVs across all scenarios. And HEVs, PHEVs and Smart Cars are estimated to represent a major share of the fleet’s VMT (25%) by year 25 under the feebate scenario. The combined share of vans, pickup trucks, sport utility vehicles (SUVs), and cross over utility vehicles (CUVs) is lowest under the feebate scenario, at 35% (versus 47% in Austin’s current household fleet), yet feebate-policy receipts exceed rebates in each simulation year. A 15% reduction in the usage levels of SUVs, CUVs and minivans is observed in the $5/gallon scenario (relative to trend). Mean use levels per vehicle of HEVs and PHEVs are simulated to have a variation of 753 and 495 across scenarios. In the longer term, gas price dynamics, tax incentives, feebates and purchase prices along with new technologies, government-industry partnerships, and more accurate information on range and recharging times (which increase customer confidence in EV technologies) should have even more significant effects on energy dependence and greenhouse gas emissions. / text

Transportation fleet emissions have a dominant role in air quality because of their significant contribution to ozone precursor and greenhouse gas emissions. Regulatory policies have emphasized improvements in vehicle fuel economy, alternative fuel use, and engine and vehicle technologies as approaches for obtaining transportation systems that support sustainable development. This study examined the air quality impacts of the partial electrification of the transportation fleet and the use of biofuels for the Austin Metropolitan Statistical Area under a 2030 vision of regional population growth and urban development using the Comprehensive Air Quality Model with extensions (CAMx). Different strategies were considered including the use of Plug-in Hybrid Electric Vehicles (PHEVs) with nighttime charging using excess capacity from electricity generation units and the replacement of conventional petroleum fuels with different percentages of the biofuels E85 and B100 along or in combination. Comparisons between a 2030 regional vision of growth assuming a continuation of current development trends (denoted as Envision Central Texas A or ECT A) in the Austin MSA and the electrification and biofuels scenarios were evaluated using different metrics, including changes in daily maximum 1-hour and 8-hour ozone concentrations, total area, time integrated area and total daily population exposure exceeding different 1-hour ozone concentration thresholds. Changes in ozone precursor emissions and predicted carbon monoxide and aldehyde concentrations were also determined for each scenario. Maximum changes in hourly ozone concentration from the use of PHEVs ranged from -8.5 to 2.2 ppb relative to ECT A. Replacement of petroleum based fuels with E85 had a lesser effect than PHEVs on maximum daily ozone concentrations. The maximum reduction due to replacement of 100% of gasoline fuel in light and heavy duty gasoline vehicles by E85 ranged from -2.1 to 2.8 ppb. The magnitude of the effect was sensitive to the biofuel penetration level. Unlike E85, B100 negatively impacted hourly ozone concentrations relative to the 2030 ECT A case. As the replacement level of petroleum-diesel fuel with B100 in diesel vehicles increased, hourly ozone concentrations increased as well. However, changes due to the penetration of B100 were relatively smaller than those due to E85 since the gasoline fraction of the fleet is larger than the diesel fraction. Because of the reductions in NOx emissions associated with E85, the results for the biofuels combination scenario were similar to those for the E85 scenario. Also, the results showed that as the threshold ozone concentration increased, so too did the percentage reductions in total daily population exposure for the PHEV, E85, and biofuel combination scenarios relative to ECT A. The greatest reductions in population exposure under higher threshold ozone concentrations were achieved with the E85 100% and 17% PHEV with EGU controls scenarios, while the B100 scenarios resulted in greater population exposure under higher threshold ozone concentrations. / text

Increasing debates over a gasoline independent future and the reduction of greenhouse gas (GHG) emissions has led to a surge in plug-in hybrid electric vehicles (PHEVs) being developed around the world. Due to the limited all-electric range of PHEVs, a daytime PHEV charging infrastructure will be required for most PHEVs’ daily usage. This dissertation, for the first time, presents a mixed integer mathematical programming model to solve the PHEV charging infrastructure planning (PCIP) problem. Our case study, based on the Oak Ridge National Laboratory (ORNL) campus, produced encouraging results, indicates the viability of the modeling approach and substantiates the importance of considering both employee convenience and appropriate grid connections in the PCIP problem. Unfortunately, the classical optimization methods do not scale up well to larger practical problems. In order to effectively and efficiently attack larger PCIP problems, we develop a new MASTS based TS algorithm, PCIP-TS to solve the PCIP. The results from computational experiments for the ORNL campus problem establish the dominant supremacy of the PCIP-TS method both in terms of solution quality and computational time. Additional experiments with simulated data representative of a problem that might be faced by a small city show that PCIP-TS outperforms CPLEX based optimization. Once the charging infrastructure is in place, the immediate problem is to judiciously manage this system on a daily basis. This thesis formally develops a mixed integer linear program to solve the daily the energy management problem (DEM) faced by an organization and presented results of a case study performed for ORNL campus. The results from our case study, based on the Oak Ridge National Laboratory (ORNL) campus, are encouraging and substantiate the importance of controlled PHEV fleet charging and realizing V2G capabilities as opposed to uncontrolled charging methods. Although optimal solutions are obtained, the solver requires practically unacceptable computational times for larger problems. Hence, we develop a new MASTS based TS algorithm, DEM-TS, for the DEM models. Results for ORNL campus data set prove the dominant computational efficiency of the DEM-TS. For the simulated extended sized problems that resemble the complexity of a problem faced by a small city, the results prove that DEM-T not only achieves optimality, but also produces sets of multiple alternate optimal solutions. These could be very helpful in practical settings when alternate solutions are necessary because some solutions may not be deployable due to unforeseen circumstances. / text

abstract: The automotive industry is committed to moving towards sustainable modes of transportation through electrified vehicles to improve the fuel economy with a reduced carbon footprint. In this context, battery-operated hybrid, plug-in hybrid and all-electric vehicles (EVs) are becoming commercially viable throughout the world. Lithium-ion (Li-ion) batteries with various active materials, electrolytes, and separators are currently being used for electric vehicle applications. Specifically, lithium-ion batteries with Lithium Iron Phosphate (LiFePO4 - LFP) and Lithium Nickel Manganese Cobalt Oxide (Li(NiMnCo)O2 - NMC) cathodes are being studied mainly due to higher cycle life and higher energy density values, respectively. In the present work, 26650 Li-ion batteries with LFP and NMC cathodes were evaluated for Plug-in Hybrid Electric Vehicle (PHEV) applications, using the Federal Urban Driving Schedule (FUDS) to discharge the batteries with 20 A current in simulated Arizona, USA weather conditions (50 ⁰C & <10% RH). In addition, 18650 lithium-ion batteries (LFP cathode material) were evaluated under PHEV mode with 30 A current to accelerate the ageing process, and to monitor the capacity values and material degradation. To offset the high initial cost of the batteries used in electric vehicles, second-use of these retired batteries is gaining importance, and the possibility of second-life use of these tested batteries was also examined under constant current charge/discharge cycling at 50 ⁰C. The capacity degradation rate under the PHEV test protocol for batteries with NMC-based cathode (16% over 800 cycles) was twice the degradation compared to batteries with LFP-based cathode (8% over 800 cycles), reiterating the fact that batteries with LFP cathodes have a higher cycle life compared to other lithium battery chemistries. Also, the high frequency resistance measured by electrochemical impedance spectroscopy (EIS) was found to increase significantly with cycling, leading to power fading for both the NMC- as well as LFP-based batteries. The active materials analyzed using X-ray diffraction (XRD) showed no significant phase change in the materials after 800 PHEV cycles. For second-life tests, these batteries were subjected to a constant charge-discharge cycling procedure to analyze the capacity degradation and materials characteristics. / Dissertation/Thesis / Masters Thesis Materials Science and Engineering 2017

No description available.

Résumé : Depuis la fin du XXème siècle, l’augmentation du prix du pétrole brut et les problématiques environnementales poussent l’industrie automobile à développer des technologies plus économes en carburant et générant moins d’émissions de gaz à effet de serre. Parmi ces technologies, les véhicules électriques hybrides constituent une solution viable et performante. En alliant un moteur électrique et un moteur à combustion, ces véhicules possèdent un fort potentiel de réduction de la consommation de carburant sans sacrifier son autonomie. La présence de deux moteurs et de deux sources d’énergie requiert un contrôleur, appelé système de gestion d’énergie, responsable de la commande simultanée des deux moteurs. Les performances du véhicule en matière de consommation dépendent en partie de la conception de ce contrôleur. Les véhicules électriques hybrides rechargeables, plus récents que leur équivalent non rechargeable, se distinguent par l’ajout d’un chargeur interne permettant la recharge de la batterie pendant l’arrêt du véhicule et par conséquent la décharge de celle-ci au cours d’un trajet. Cette particularité ajoute un degré de complexité pour ce qui est de la conception du système de gestion d’énergie. Dans cette thèse, nous proposons un modèle complet du véhicule dédié à la conception du contrôleur. Nous étudions ensuite la dépendance de la commande optimale des deux moteurs par rapport au profil de vitesse suivi au cours d’un trajet ainsi qu’à la quantité d’énergie électrique disponible au début d’un trajet. Cela nous amène à proposer une technique d’auto-apprentissage visant l’amélioration de la stratégie de gestion d’énergie en exploitant un certain nombre de données enregistrées sur les trajets antérieurs. La technique proposée permet l’adaptation de la stratégie de contrôle vis-à-vis du trajet en cours en se basant sur une pseudo-prédiction de la totalité du profil de vitesse. Nous évaluerons les performances de la technique proposée en matière de consommation de carburant en la comparant avec une stratégie optimale bénéficiant de la connaissance exacte du profil de vitesse ainsi qu’avec une stratégie de base utilisée couramment dans l’industrie. // Abstract : Since the end of the XXth century, the increase in crude oil price and the environmental concerns lead the automotive industry to develop technologies that can improve fuel savings and decrease greenhouse gases emissions. Among these technologies, the hybrid electric vehicles stand as a reliable and efficient solution. By combining an electrical motor and an internal combustion engine, these vehicles can bring a noticeable improvement in terms of fuel consumption without sacrificing the vehicle autonomy. The two motors and the two energy storage systems require a control unit, called energy management system, which is responsible for the command decision of both motors. The vehicle performances in terms of fuel consumption greatly depend on this control unit. The plug-in hybrid electric vehicles are a more recent technology compared to their non plug-in counterparts. They have an extra internal battery charger that allows the battery to be charged during OFF state, implying a possible discharge during a trip. This particularity adds complexity when it comes to the design of the energy management system. In this thesis, a complete vehicle model is proposed and used for the design of the controller. A study is then carried out to show the dependence between the optimal control of the motors and the speed profile followed during a trip as well as the available electrical energy at the beginning of a trip. According to this study, a self-learning optimization technique that aims at improving the energy management strategy by exploiting some driving data recorded on previous trips is proposed. The technique allows the adaptation of the control strategy to the current trip based on a pseudo-prediction of the total speed profile. Fuel consumption performances for the proposed technique will be evaluated by comparing it with an optimal control strategy that benefits from the exact a priori knowledge of the speed profile as well as a basic strategy commonly used in industry.

The purpose of study is to research the development of electric vehicle technology in the United States. This study describes the United States public policies towards electric vehicle technology and system of innovation approaches. The government roles with the help of national system of innovation have been also covered in this study. The point of departure was the study of available literature and U.S energy policy acts which illustrates that the break-through in electric vehicles still not only depended on better battery technology and infrastructure for charging stations but also on social, economic and political factors. The important actors involved in the process are both at local and international level are private firms, governmental departments, research and development (R&amp;D) institutes, nongovernment organizations (NGO’s) and environmental organizations etc. The arguments which are put forward in the background of development of such technologies are to reduce dependence on foreign oil and to reduce emissions of harmful gasses.

This article explores the potential of using real-world driving patterns to derive PHEV and BEV utility factors and evaluates how different travel and recharging behaviours affect the calculation of the standard SAE J2841 utility factor. The study relies on six datasets of driving data collected monitoring 508,607 conventional fuel vehicles in six European areas and a dataset of synthetic data from 700,000 vehicles in a seventh European area. Sources representing the actual driving behaviour of PHEV together with the WLTP European utility factor are adopted as term of comparison. The results show that different datasets of driving data can yield to different estimates of the utility factor. The SAE J2841 standard method results to be representative of a large variety of behaviours of PHEVs and BEVs' drivers, characterised by a fully-charged battery at the beginning of the trip sequence, thus being representative for fuel economy and emission estimates in the early phase deployment of EVs, charged at home and overnight. However the results show that the SAE J2841 utility factor might need to be revised to account for more complex future scenarios, such as necessity-driven recharge behaviour with less than one recharge per day or a fully deployed recharge infrastructure with more than one recharge per day.