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

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

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%.

During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range. / MS / During the past two decades desperate need for energy-efficient vehicles which has less emission have led to a great attention to and development of electrified vehicles like pure electric, Hybrid Electric Vehicle (HEV) and Plug-in Hybrid Electric Vehicles (PHEVs). Resultantly, a great amount of research efforts have been dedicated to development of control strategies for this type of vehicles including PHEV which is the case study in this thesis. This thesis presents a real-time control scheme to improve the fuel economy of plug-in hybrid electric vehicles (PHEVs) by accounting for the instantaneous states of the system as well as the future trip information. To design the mentioned parametric real-time power management policies, we use dynamic programming (DP). First, a representative power-split PHEV powertrain model is introduced, followed by a DP formulation for obtaining the optimal powertrain trajectories from the energy cost point of view for a given drive cycle. The state and decision variables in the DP algorithm are selected in a way that provides the best tradeoff between the computational time and accuracy which is the first contribution of this research effort. These trajectories are then used to train a set of linear maps for the powertrain control variables such as the engine and electric motor/generator torque inputs, through a least-squares optimization process. The DP results indicate that the trip length (distance from the start of the trip to the next charging station) is a key factor in determining the optimal control decisions. To account for this iv factor, an additional input variable pertaining to the remaining length of the trip is considered during the training of the real-time control policies. The proposed controller receives the demanded propulsion force and the powertrain variables as inputs, and generates the torque commands for the engine and the electric drivetrain system. Numerical simulations indicate that the proposed control policy is able to approximate the optimal trajectories with a good accuracy using the real-time information for the same drive cycles as trained and drive cycle out of training set. To maintain the battery state-of-charge (SOC) above a certain lower bound, two logics have been introduced a switching logic is implemented to transition to a conservative control policy when the battery SOC drops below a certain threshold. Simulation results indicate the effectiveness of the proposed approach in achieving near-optimal performance while maintaining the SOC within the desired range.

Today, hybrid electric propulsion technology provides a promising and practical solution for improving vehicle performance, increasing energy efficiency, and reducing harmful emissions, due to the additional flexibility that the technology has provided in the optimal power control and energy management, which are the keys to its success. In this work, a systematic approach for real-time optimal energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) has been introduced and validated through two HEV/PHEV case studies. Firstly, a new analytical model of the optimal control problem for the Toyota Prius HEV with both offline and real-time solutions was presented and validated through Hardware-in-Loop (HIL) real-time simulation. Secondly, the new online or real-time optimal control algorithm was extended to a multi-regime PHEV by modifying the optimal control objective function and introducing a real-time implementable control algorithm with an adaptive coefficient tuning strategy. A number of practical issues in vehicle control, including drivability, controller integration, etc. are also investigated. The new algorithm was also validated on various driving cycles using both Model-in-Loop (MIL) and HIL environment. This research better utilizes the energy efficiency and emissions reduction potentials of hybrid electric powertrain systems, and forms the foundation for development of the next generation HEVs and PHEVs. / Graduate / laindeece@gmail.com

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

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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