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

Following the Model-Based-Design (MBD) development process used presently by the automotive industry, the control systems for a new Series-Parallel Multiple-Regime Plug-in Hybrid Electric Vehicle (PHEV), UVic EcoCAR2, have been developed, implemented and tested. Concurrent simulation platforms were used to achieve different developmental goals, with a simplified system power loss model serving as the low-overhead control strategy optimization platform, and a high fidelity Software-in-Loop (SIL) model serving as the vehicle control development and testing platform. These two platforms were used to develop a strategy-independent controls development tool which will allow deployment of new strategies for the vehicle irrespective of energy management strategy particulars. A rule-based energy management strategy was applied and calibrated using genetic algorithm (GA) optimization. The concurrent modeling approach was validated by comparing the vehicle equivalent fuel consumption between the simplified and SIL models. An equivalency factor (EF) of 1 was used in accounting for battery state of charge (SOC) discrepancies at cycle end. A recursively-defined subsystem efficiency-based EF was also applied to try to capture real-world equivalency impacts. Aggregate results between the two test platforms showed translation of the optimization benefits though absolute results varied for some cycles. Accuracy improvements to the simplified model to better capture dynamic effects are recommended to improve the utility of the newly introduced vehicle control system development method. Additional future work in redefining operation modes and mode transition threshold conditions to approximate optimal vehicle operation is recommended and readily supported by the control system platform developed. / Graduate / 0540 / 0548 / 0790 / d.e.prescott@gmail.com

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

The content of this thesis is analysis of energy flows of the propulsion system in plug-in hybrid utility vehicle. Theoretical part of the thesis deals with electrically assisted turbochargers, which can positively influence engine characteristics. The following part analyses given diesel engine and utility vehicle in simulations in GT-Suite software, with simulations being performed on WLTC driving cycle. The results of the simulations demonstrate the relation between the usage of electrical and fuel energies and the driving cycle and show the engine operation points. By using optimal battery capacity, it is possible to save according to driving cycle more than 50 % energy consumed by the engine, directly influencing the fuel consumption. The results are possible to use as a guidance for moving the engine operation points and for sizing of the battery pack of hybrid utility vehicle according to real life application.

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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