Hybrid electric vehicle technology has become a preferred method for the automotive industry to reduce environmental impact and fuel consumption of their vehicles. Hybrid electric vehicles accomplish these reductions through the use of multiple propulsion systems, namely an electric motor and internal combustion engine, which allow the elimination of idling, operation of the internal combustion engine in a more efficient manner and the use of regenerative braking. However, the added cost of the hybrid electric system has hindered the sales of these vehicles. A more cost effective design of an electro-hydraulic braking system is presented. The system electro-mechanically controlled the boost force created by the brake booster independently of the driver braking force and with adequate time response. The system allowed for the blending of the mechanical and regenerative braking torques in a manner transparent to the driver and allowed for regenerative braking to be conducted efficiently. A systematic design process was followed, with emphasis placed on demonstrating conceptual design feasibility and preliminary design functionality using virtual and physical prototyping. The virtual and physical prototypes were then used in combination as a powerful tool to validate and develop the system. The role of prototyping in the design process is presented and discussed. Through the experiences gained by the author during the design process, it is recommended that students create physical prototypes to enhance their educational experience. These experiences are evident throughout the thesis presented.

The main objective of this research is the development of a flexible test-bench for evaluation of hybrid electric powertrain controllers. As a case study, a real-time near-optimal powertrain controller for a series hybrid electric vehicle (HEV) has been designed and tests. The designed controller, like many other optimal controllers, is based on a simple model. This control-oriented model aims to be as simple as possible in order to minimize the controller computational effort. However, a simple model may not be able to capture the vehicle's dynamics accurately, and the designed controller may fail to deliver the anticipated behavior. Therefore, it is crucial that the controller be tested in a realistic environment. To evaluate the performance of the designed model-based controller, it is first applied to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. After successfully passing this model-in-the-loop test, the controller is programmed into a rapid-prototyping controller unit for hardware-in-the-loop simulations. This type of simulation is mostly intended to consider controller computational resources, as well as the communication issues between the controller and the plant (model solver). As the battery pack is one of the most critical components in a hybrid electric powertrain, the component-in-the-loop simulation setup is used to include a physical battery in the simulations in order to further enhance simulation accuracy. Finally, the driver-in-the-loop setup enables us to receive the inputs from a human driver instead of a fixed drive cycle, which allows us to study the effects of the unpredictable driver behavior. The developed powertrain controller itself is a real-time, drive cycle-independent controller for a series HEV, and is designed using a control-oriented model and Pontryagin's Minimum Principle. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions; however, the amount of information is reduced. Although the controller design procedure is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to obtain the supervisory controller for a series HEV with an ultra-capacitor. By testing the designed optimal controller with the prescribed simulation setups, it is shown that the controller can ensure optimal behavior of the powertrain, as the dominant system behavior is very close to what is being predicted by the control-oriented model. It is also shown that the controller is able to handle small uncertainties in the driver behavior.

A plug-in hybrid electric vehicle (PHEV) is a vehicle powered by a combination of an internal combustion engine and an electric motor with a battery pack. The battery pack can be charged by plugging the vehicle into the electric grid or from using excess engine power. A PHEV allows for all electric operation for limited distances, while having the operation and range of a conventional hybrid electric vehicle on longer trips. A PHEV design with design parameters electric motor size, engine size, battery capacity, and battery chemistry type, is optimized with minimum cost as a figure of merit. The PHEV is required to meet a fixed set of performance constraints consisting of 0-60 mph acceleration, 50-70 mph acceleration, 0-30 mph acceleration in all electric operation, top speed, grade ability, and all electric range. The optimization is carried out for values of all electric range of 10, 20, and 40 miles. The social and economic impacts of the optimum designs in terms of reduced gasoline consumption and carbon emissions reduction are calculated. Argonne National Laboratorys Powertrain Systems Analysis Toolkit is used to simulate the performance and fuel economy of the PHEV designs. The costs of different PHEV components and the present value of battery replacements over the vehicles life are used to determine the designs drivetrain cost. The resulting optimum PHEVs are designs using lead acid battery type. The optimum design parameter values are all determined by a single controlling performance constraint. The PHEV designs show a 63% to 80% reduction in gasoline consumption and a 53% to 47% reduction in CO2 emissions. The PHEV designs have an annual gas savings of $696 to $643 per year over the average sedan meeting the 27.5 mpg CAFE standards.

With increasing interest in energy storage and conversion devices for automobile applications, the necessity to understand and predict life behavior of rechargeable batteries, PEM fuel cells and super capacitors is paramount. These electrochemical devices are most beneficial when used in hybrid configurations rather than as individual components because no single device can meet both range and power requirements to effectively replace internal combustion engines for automobile applications. A system model helps us to understand the interactions between components and enables us to determine the response of the system as a whole. However, system models that are available predict just the performance and neglect degradation. In the first part of the thesis, a framework is provided to account for the durability phenomena that are prevalent in fuel cells and batteries in a hybrid system. Toward this end, the methodology for development of surrogate models is provided, and Pt catalyst dissolution in PEMFCs is used as an example to demonstrate the approach. Surrogate models are more easily integrated into higher level system models than the detailed physics-based models. As an illustration, the effects of changes in control strategies and power management approaches in mitigating platinum instability in fuel cells are reported. A system model that includes a fuel cell stack, a storage battery, power-sharing algorithm, and dc/dc converter has been developed; and preliminary results have been presented. These results show that platinum stability can be improved with only a small impact on system efficiency. Thus, this research will elucidate the importance of degradation issues in system design and optimization as opposed to just initial performance metrics. In the second part of the thesis, modeling of silicon negative electrodes for lithium ion batteries is done at both particle level and cell level. The dependence of the open-circuit potential curve on the state of charge in lithium insertion electrodes is usually measured at equilibrium conditions. Firstly, for modeling of lithium-silicon electrodes at room temperature, the use of a pseudo-thermodynamic potential vs. composition curve based on metastable amorphous phase transitions with path dependence is proposed. Volume changes during lithium insertion/de-insertion in single silicon electrode particle under potentiodynamic control are modeled and compared with experimental data to provide justification for the same. This work stresses the need for experiments for accurate determination of transfer coefficients and the exchange current density before reasoning kinetic hysteresis for the potential gap in Li-Si system. The silicon electrode particle model enables one to analyze the influence of diffusion in the solid phase, particle size, and kinetic parameters without interference from other components in a practical porous electrode. Concentration profiles within the silicon electrode particle under galvanostatic control are investigated. Sluggish kinetics is established from cyclic voltammograms at different scan rates. Need for accurate determination of exchange current density for lithium insertion in silicon nanoparticles is discussed. This model and knowledge thereof can be used in cell-sandwich model for the design of practical lithium ion cells with composite silicon negative electrodes. Secondly, galvanostatic charge and discharge of a silicon composite electrode/separator/ lithium foil is modeled using porous electrode theory and concentrated solution theory. Porosity changes arising due to large volume changes in the silicon electrode with lithium insertion and de-insertion are included and analyzed. The concept of reservoir is introduced for lithium ion cells to accommodate the displaced electrolyte. Influence of initial porosity and thickness of the electrode on utilization at different rates is quantitatively discussed. Knowledge from these studies will guide design of better silicon negative electrodes to be used in dual lithium insertion cells for practical applications.

This dissertation identifies the average treatment effect of state level incentives for hybrid vehicles, identifies individual-level predictors of early adopters, and attempts to understand why states adopt these incentives. These questions are estimated using traditional parametric techniques, logistic regression, difference-in-difference regression, and fixed effects. In particular, this dissertation looks at changes in aggregate demand on two comparison groups: (1) the natural control group, states that did not adopt subsidies, and (2) a constructed control group, states that proposed subsidies during this same time period but did not adopt them. In addition to these parametric models, propensity score matching was used to construct a third comparison group using the models that identified determinants of the policy adoption. These findings were supplemented by exploratory analyses using the individual-level National Household Travel Survey. This multitude of evaluative analyses shows that HOV lane exemptions, if implemented in places with high traffic congestion, were found to impact aggregate demand and an individual's propensity to adopt a hybrid, while traditional incentives had limited impact. These analyses provide insight into why states adopt certain policies and the circumstances in which these incentives are effective. Since people may be motivated by factors other than economic factors, creating effective incentives for energy efficiency technologies may be more challenging than just offsetting the price differential. Instead, customization to the local community's characteristics could help increase the efficacy of such policies.

Energy-storing electrochemical batteries are the most critical components of high energy density storage systems for stationary and mobile applications. Lithium-ion batteries have received considerable interest for hybrid electric vehicles (HEV) because of their high specific energy, but face inherent thermal management challenges that have not been adequately addressed. In the present investigation, a fully coupled electrochemical and thermal model for lithium-ion batteries is developed to investigate the impact of different thermal management strategies on battery performance. This work represents the first ever study of these coupled electrochemical-thermal phenomena in batteries from the electrochemical heat generation all the way to the dynamic heat removal in actual HEV drive cycles. In contrast to previous modeling efforts focused either exclusively on particle electrochemistry on the one hand or overall vehicle simulations on the other, the present work predicts local electrochemical reaction rates using temperature-dependent data on commercially available batteries designed for high rates (C/LiFePO4) in a computationally efficient manner. Simulation results show that conventional external cooling systems for these batteries, which have a low composite thermal conductivity (~1 W/m-K), cause either large temperature rises or internal temperature gradients. Thus, a novel, passive internal cooling system that uses heat removal through liquid-vapor phase change is developed. Although there have been prior investigations of phase change at the microscales, fluid flow at the conditions expected here is not well understood. A first-principles based cooling system performance model is developed and validated experimentally, and is integrated into the coupled electrochemical-thermal model for assessment of performance improvement relative to conventional thermal management strategies. The proposed cooling system passively removes heat almost isothermally with negligible thermal resistances between the heat source and cooling fluid. Thus, the minimization of peak temperatures and gradients within batteries allow increased power and energy densities unencumbered by thermal limitations.

Thesis (M.S.)--West Virginia University, 2003. / Title from document title page. Document formatted into pages; contains xiii, 107 p. : ill. (some col.). Vita. Includes abstract. Includes bibliographical references (p. 102-107).

Nowadays, the increasing amount of greenhouse gases and diminishing of the existing petroleum minerals for future generations, has led the automotive companies to think of producing vehicles with less emissions and fuel consumption. For this purpose, Hybrid Electric Vehicles (HEVs) have emerged in the recent decades. HEVs with different configurations have been introduced by engineers.The simulation platform aim for a parallel HEV, where the intention is to reduce the emissions and fuel consumption. The simulation platform includes an Electric Motor (EM) in addition to an Internal Combustion Engine (ICE). A new transmission system is modeled which is compatible with parallel configuration for the HEV, where the inertial effects of the gearbox, clutch and driveline is formulated. The transmission system includes a gearbox which is equipped with synchronizers for smooth change of gears. The HEV is controlled by a rule based controller together with an optimization algorithm as power management strategy in order to have optimal fuel consumption. Using the rule based controller, the HEV is planned to be launched by EM in order to have a downsized clutch and ICE. The clutch modeling is the main focus of this study, where the slipping mechanism is considered in the simulation. In the driveline model, the flexibility effects of the propeller shaft and drive shaft is simulated, so that the model can capture the torsional vibrations of the driveline. The objective of modeling such a system is to reduce emissions and fuel consumption with the same performance of the conventional vehicle. To achieve this goal first a conventional vehicle is modeled and subsequently, a hybrid vehicle is modeled and finally the characteristics of the two simulated models are studied and compared with each other. Using the simulation platform, the state of charge (SOC) of the battery, oscillations of propeller shaft and drive shaft, clutch actuations and couplings, energy dissipated by the clutch, torques provided by EM and ICE, fuel consumptions, emissions and calculation time are calculated and investigated. The hybridization results in a reduction in fuel consumption and emissions, moreover, the energy dissipated by the clutch and clutch couplings are decreased.

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

Today uncertainties of petroleum supply and concerns over global warming call for further advancement of green vehicles with higher energy efficiency and lower green house gas (GHG) emissions. Development of advanced hybrid electric powertrain technology plays an important role in the green vehicle transformation with continuously improved energy efficiency and diversified energy sources. The added complexity of the multi-discipline based, advanced hybrid powertrain systems make traditional powertrain design method obsolete, inefficient, and ineffective. This research follows the industrial leading model-based design approach for hybrid electric vehicle powertrain development and introduces the optimization based methods to address several key design challenges in hybrid electric powertrain and its control system design. Several advanced optimization methods are applied to identify the proper hybrid powertrain architecture and design its control strategies for better energy efficiency. The newly introduced optimization based methods can considerably alleviate the design challenges, avoid unnecessary design iterations, and improve the quality and efficiency of the powertrain design. The proposed method is tested through the design and development of a prototype extended range electric vehicle (EREV), UVic EcoCAR. Developments of this advanced hybrid vehicle provide a valuable platform for verifying the new design method and obtaining feedbacks to guide the fundamental research on new hybrid powertrain design methodology. / Graduate