Plug-in Hybrid Electric vehicle (PHEV) technology has the potential to reduce operational costs, greenhouse gas (GHG) emissions, and gasoline consumption in the transportation market. However, the net benefits of using a PHEV depend critically on several aspects, such as individual travel patterns, vehicle powertrain design and battery technology. To examine these effects, a multi-objective optimization model was developed integrating vehicle physics simulations through a Matlab/Simulink model, battery durability, and Canadian driving survey data. Moreover, all the drivetrains are controlled implicitly by the ADVISOR powertrain simulation and analysis tool. The simulated model identifies Pareto optimal vehicle powertrain configurations using a multi-objective Pareto front pursuing genetic algorithm by varying combinations of powertrain components and allocation of vehicles to consumers for the least operational cost, and powertrain cost under various driving assumptions. A sensitivity analysis over the foremost cost parameters is included in determining the robustness of the optimized solution of the simulated model in the presence of uncertainty. Here, a comparative study is also established between conventional and hybrid electric vehicles (HEVs) to PHEVs with equivalent optimized solutions, size and performance (similar to Toyota Prius) under both the urban and highway driving environments. In addition, breakeven point analysis is carried out that indicates PHEV lifecycle cost must fall within a few percent of CVs or HEVs to become both the environmentally friendly and cost-effective transportation solutions. Finally, PHEV classes (a platform with multiple powertrain architectures) are optimized taking into account consumer diversity over various classes of light-duty vehicle to investigate consumer-appropriate architectures and manufacturer opportunities for vehicle fleet development utilizing simplified techno-financial analysis. / Graduate / 0540 / 0548 / ehtesham@uvic.ca

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

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Throughout the past several years, a major push has been made for the automotive industry to provide vehicles with lower environmental impacts while maintaining safety, performance, and overall appeal. Various legislation has been put into place to establish guidelines for these improvements and serve as a challenge for automakers all over the world. In light of these changes, hybrid technologies have been growing immensely on the market today as customers are seeing the benefits with lower fuel consumption and higher efficiency vehicles. With the need for hybrids rising, it is vital for the engineers of this age to understand the importance of advanced vehicle technologies and learn how and why these vehicles can change the world as we know it. To help in the education process, this thesis seeks to define a powertrain model created and developed to help users understand the basics behind hybrid vehicles and the effects of these advanced technologies. One of the main goals of this research is to maintain a simplified approach to model development. There are very complex vehicle simulation models in the market today, however these can be hard to manipulate and even more difficult to understand. The 1 Hz model described within this work aims to allow energy to be simply and understandable traced through a hybrid powertrain. Through the use of a 'backwards' energy tracking method, demand for a drive cycle is found using a drive cycle and vehicle parameters. This demand is then used to determine what amount of energy would be required at each component within the powertrain all the way from the wheels to the fuel source, taking into account component losses and accessory loads on the vehicle. Various energy management strategies are developed and explained including controls for regenerative braking, Battery Electric Vehicles, and Thermostatic and Load-following Series Hybrid Electric Vehicles. These strategies can be easily compared and manipulated to understand the tradeoffs and limitations of each. After validating this model, several studies are completed. First, an example of using this model to design a hybrid powertrain is conducted. This study moves from defining system requirements to component selection, and then finding the best powertrain to accomplish the given constraints. Next, a parameter known as Power Split Fraction is studied to provide insight on how it affects overall powertrain efficiency. Since the goal with advanced vehicle powertrains is to increase overall system efficiency and reduce overall energy consumption, it is important to understand how all of the factors involved affect the system as a whole. After completing these studies, this thesis moves on to discussing future work which will continue refining this model and making it more applicable for design. Overall, this work seeks to provide an educational tool and aid in the development of the automotive engineers of tomorrow. / Master of Science

As the global demand for energy increases, the people of the United States are increasingly subject to high and ever-rising oil prices. Additionally, the U.S. transportation sector accounts for 27% of total nationwide Greenhouse Gas (GHG) emissions. In the U.S. transportation sector, light-duty passenger vehicles account for about 58% of energy use. Therefore incremental improvements in light-duty vehicle efficiency and energy use will significantly impact the overall landscape of energy use in America. A crucial step to designing and building more efficient vehicles is modeling powertrain energy consumption. While accurate modeling is indeed key to effective and efficient design, a fundamental understanding of the powertrain and auxiliary systems that contribute to energy consumption for a vehicle is equally as important if not more important. This thesis presents a methodology that has been packaged into a tool, called VTool, that can be used to estimate the energy consumption of a vehicle powertrain. The method is intrinsically designed to foster understanding of the vehicle powertrain as it relates to energy consumption while still providing reasonably accurate results. VTool explicitly calculates the energy required at the wheels of the vehicle to complete a prescribed drive cycle and then explicitly applies component efficiencies to find component losses and the overall energy consumption for the drive cycle. In calculating component efficiencies and losses, VTool offers several tunable parameters that can be used to calibrate the tool for a particular vehicle, compare powertrain architectures, or simply explore the tradeoffs and sensitivities of certain parameters. In this paper, the method is fully and explicitly developed to model Electric Vehicles (EVs), Series Hybrid Electric Vehicles (HEVs) and Parallel HEVs for various different drive cycles. VTool has also been validated for use in UDDS and HwFET cycles using on-road test results from the 2011 EcoCAR competition. By extension, the method could easily be extended for use in other cycles. The end result is a tool that can predict fuel consumption to a reasonable degree of accuracy for a variety of powertrains, calculate J1711 Utility Factor weighted energy consumption for Extended Range Electric Vehicles (EREVs) and determine the Well-to-Wheel impact of a given powertrain or fuel. VTool does all of this while performing all calculations explicitly and calculating all component losses to allow the user maximum access which promotes understanding and comprehension of the fundamental dynamics of automotive fuel economy and the powertrain as a system. / Master of Science

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.

Transportation is one of the sectors that directly touches the major challenges that energy utilities are faced with, namely, the significant increase in energy demand and environmental issues. In view of these concerns and the problems with the supply of oil, the pursuit of alternative fuels for meeting the future energy demand of the transport sector has gained much attention. The future of transportation is believed to be based on electric drives in fuel cell vehicles (FCVs) or plug-in electric vehicles (PEVs). There are compelling reasons for this to happen: the efficiency of electric drive is at least three times greater than that of combustion processes and these vehicles produce almost zero emissions, which can help relieve many environmental concerns. The future of PEVs is even more promising because of the availability of electricity infrastructure. Furthermore, governments around the world are showing interest in this technology by investing billions of dollars in battery technology and supportive incentive programs for the customers to buy these vehicles. In view of all these considerations, power systems specialists must be prepared for the possible impacts of these new types of loads on the system and plan for the optimal transition to these new types of vehicles by considering the electricity grid constraints. Electricity infrastructure is designed to meet the highest expected demand, which only occurs a few hundred hours per year. For the remaining time, in particular during off-peak hours, the system is underutilized and could generate and deliver a substantial amount of energy to other sectors such as transport by generating hydrogen for FCVs or charging the batteries in PEVs. This thesis investigates the technical and economic feasibility of improving the utilization of electricity system during off-peak hours through alternative-fuel vehicles (AFVs) and develops optimization planning models for the transition to these types of vehicles. These planning models are based on decomposing the region under study into different zones, where the main power generation and electricity load centers are located, and considering the major transmission corridors among them. An emission cost model of generation is first developed to account for the environmental impacts of the extra load on the electricity grid due to the introduction of AFVs. This is followed by developing a hydrogen transportation model and, consequently, a comprehensive optimization model for transition to FCVs in the context of an integrated electricity and hydrogen system. This model can determine the optimal size of the hydrogen production plants to be developed in different zones in each year, optimal hydrogen transportation routes and ultimately bring about hydrogen economy penetration. This model is also extended to account for optimal transition to plug-in hybrid electric vehicles (PHEVs). Different aspects of the proposed transition models are discussed on a developed 3-zone test system. The practical application of the proposed models is demonstrated by applying them to Ontario, Canada, with the purpose of finding the maximum potential penetrations of AFVs into Ontario’s transport sector by 2025, without jeopardizing the reliability of the grid or developing new infrastructure. Applying the models to this real-case problem requires the development of models for Ontario’s transmission network, generation capacity and base-load demand during the planning study. Thus, a zone-based model for Ontario’s transmission network is developed relying on major 500 and 230 kV transmission corridors. Also, based on Ontario’s Integrated Power System Plan (IPSP) and a variety of information provided by the Ontario Power Authority (OPA) and Ontario’s Independent Electricity System Operator (IESO), a zonal pattern of base-load generation capacity is proposed. The optimization models developed in this study involve many parameters that must be estimated; however, estimation errors may substantially influence the optimal solution. In order to resolve this problem, this thesis proposes the application of robust optimization for planning the transition to AFVs. Thus, a comprehensive sensitivity analysis using Monte Carlo simulation is performed to find the impact of estimation errors in the parameters of the planning models; the results of this study reveals the most influential parameters on the optimal solution. Having a knowledge of the most affecting parameters, a new robust optimization approach is applied to develop robust counterpart problems for planning models. These models address the shortcoming of the classical robust optimization approach where robustness is ensured at the cost of significantly losing optimality. The results of the robust models demonstrate that with a reasonable trade-off between optimality and conservatism, at least 170,000 FCVs and 900,000 PHEVs with 30 km all-electric range (AER) can be supported by Ontario’s grid by 2025 without any additional grid investments.