The batteries used in plug-in hybrid electric vehicles (PHEVs) need to overcome significant technical challenges in order for PHEVs to become economically viable and have a large market penetration. The internship at Argonne National Laboratory (ANL) involved two experiments which looked at a vehicle systems approach to analyze two such technical challenges: Battery life and low battery power at cold (-7 ⁰C) temperature. The first experiment, concerning battery life and its impact on gasoline savings due to a PHEV, evaluates different vehicle control strategies over a pre-defined vehicle drive cycle, in order to identify the control strategy which yields the maximum dollar savings (operating cost) over the life of the vehicle, when compared to a charge sustaining hybrid. Battery life degradation over the life of the vehicle, and fuel economy savings on every trip (daily) are taken into account when calculating the net present value of the gasoline dollars saved. The second experiment evaluates the impact of different vehicle control strategies in heating up the PHEV battery (due to internal ohmic losses) for cold ambient conditions. The impact of low battery power (available to the vehicle powertrain) due to low battery and ambient temperatures has been well documented in literature. The trade-off between the benefits of heating up the battery versus heating up the internal combustion engine are evaluated, using different control strategies, and the control strategy, which provided optimum temperature rise of each component, is identified.

The powertrain design and control strategies for three representative hybrid and plug-in hybrid electric vehicles (HEV/PHEVs), a plug-in hybrid passenger car, a plug-in hybrid race car, and a hybrid electric mining truck, have been investigated through the system modeling, simulation and design optimization. First, the pre-transmission gen-set couple Plug-in Series-Parallel Multi-Regime (SPMR) powertrain architecture was selected for PHEV passenger car. Rule-based load following control schemes based on engine optimal control strategy and Equivalent Consumption Minimization Strategy (ECMS) were used for the operation control of the passenger car PHEV powertrain. Secondly, the rear wheel drive (RWD) post-transmission parallel through road powertrain architecture was selected for race car PHEV. A high level supervisory control system and ECMS control strategy have been developed and implemented through the race car’s on-board embedded controller using dSPACE MicroAutobox II. In addition, longitudinal adaptive traction control has been added to the vehicle controller for improved drivability and acceleration performance. At last, the feasibility and benefits of powertrain hybridization for heavy-duty mining truck have been investigated, and three hybrid powertrain architectures, series, parallel and diesel-electric, with weight adjusting propulsion system have been modeled and studied. The research explored the common and distinct characteristics of hybrid electric propulsion system technology for different vehicular applications, and formed the foundation for further research and development. / Graduate / 0540 / ruicheng@uvic.ca

Georgia Tech has the privilege of competing in EcoCAR 3, a four-year competition in which 16 universities are given a stock 2016 Chevrolet Camaro and work to transform it into a hybrid electric sports car. In this thesis, an overview of the first two years of the author’s work on the team as the Engineering Manager, the graduate student overseeing all vehicle engineering work, will be detailed. The competition will be introduced and described before a discussion on vehicle electrification and the various ways it has been achieved by manufacturers and competition teams. Next, the design of the Georgia Tech vehicle will be presented with a focus on powertrain and supporting component selection. The vehicle model underlying many of these decisions will then be discussed in detail, showing how the team used Simulink and Engineering Equation Solver to effectively predict vehicle performance, emissions, energy consumption, and cooling needs. Building on this, the controls design process known as model/software/hardware in the loop will be discussed in the context of the Georgia Tech team’s use of this process. Finally, a progress update will be given, including photos of the team vehicle in current build state weeks before the Year 2 Competition.

Very little information is known about the impact electrification has on driving behavior, or how drivers charge their electrified vehicles. The recent influx of electrified vehicles presents a new market of vehicles which allow drivers the option between electrical or conventional gasoline energy sources. The current battery capacity in full battery electric vehicles requires planning of routes not required of conventional vehicles, due to the limited range, extended charging times, and limited charging infrastructure. There is currently little information on how drivers react to these limitations. A number of current models of fully electric and plug-in hybrid electric vehicles, transmit data wirelessly on key-on, key-off, and charging events. The data includes battery state of charge, distance of miles driven on gasoline and electric, energy consumed, and many other parameters associated to driving and charging behavior. In this thesis, this data was then processed and analyzed to benchmark the performance and characteristics of driving and charging patterns. Vehicles were analyzed and contrasted based on model type, geographic location, length of ownership and other variables. This data was able to show benchmarks and parameters in aggregate for 56 weeks of electrified vehicle tracking. These parameters were compared to the EV Project, a large scale electrified vehicle study performed by Idaho National Labs, to confirm patterns of expected behavior. New parameters which were not present in the EV Project were analyzed and provided insight to charging and driving behavior not examined in any previous study on a large scale. This study provides benchmarks and conclusions on this new driving behavior, such as large scale analysis of brake regeneration performance and degradation of range anxiety. Analysis of the differences on charging and driving behavior between geographic regions and experience were examined, providing insight to how these variables affect performance and driving and charging patterns. Comparison of parameters established by the EV Project and new parameters analyzed in this report will help build a benchmark for future studies of electrified vehicles.

Range anxiety is a relatively new concept which is defined as the fear of running out of power when driving an electric vehicle. To decrease range anxiety you can increase the battery size or decrease the minimum state of charge, the least amount of power that can be left in the battery, or to expand the available fast charging infrastructure. But is that economical feasible or even technically possible in today’s society? In this project we have used a theoretical model for estimating range anxiety and have simulated the average electricity consumption using two different kinds of electric vehicles, to see how often they reach range anxiety according to a specific definition of range anxiety implemented in this model. The simulations were performed for different scenarios in order to evaluate the effect of different parameters on range anxiety. The result that we got were that range anxiety can be decreased with bigger batteries but to get range anxiety just a few times a year you have to use battery sizes which aren’t economical feasible today. Despite the shortcomings of todays electric vehicles there are promising new and future technologies such as better batteries which might help alleviate range anxiety for electric vehicle owner. The conclusion from this study is that in the present fleet of electric vehicles is in need of more charging stations and faster charging to get by the problem with range anxiety and having a chance to compete with gasoline and diesel vehicles.

Increase in energy demand is one of the major challenges that utilities are faced with, thus resulting in an increase in environmental pollution and global warming. The transport sector has a significant share of the energy demand and is a major contribution of emissions to the environment. In Canada, almost 35% of the total energy demand is from the transport sector and it is the second largest source of greenhouse gas (GHG) emissions. The government of Ontario has aimed to move toward a green energy economy, thus resulting in increased penetration of renewable energy sources as well as Plug-in hybrid electric vehicle (PHEV) technology. Penetration of renewable energy sources into microgrids are gradually being recognized as important alternatives in supply side planning. This thesis focuses on the optimal design, planning, sizing and operation of a hybrid, renewable energy based microgrid with the goal of minimizing the lifecycle cost, while taking into account environmental emissions. Four different configurations including a diesel-only, a fully renewable-based, a diesel-renewable mixed, and an external-grid connected microgrid are designed, to compare and evaluate their economics, operational performance and environmental emissions. Analysis is also carried out to determine the break-even economics for a grid-connected microgrid. The well-known energy modeling software for hybrid renewable energy systems, HOMER, is used in the studies reported in this thesis. An optimal power flow (OPF) based optimization framework considering two different objectives, minimizing feeder losses and PHEV charging cost, are presented to understand the impact of PHEV charging on distribution networks. Three different charging periods are considered and the impact of the Ontario Time-of-Use (TOU) tariff on PHEV charging schedules is examined. The impact of PHEV charging on distribution systems in the presence of renewable energy sources is discussed by extending the developed OPF based model to include the contribution of renewable energy sources. The proposed model is evaluated under a variety of scenarios.

Plug-in hybrid electric vehicles (PHEV) with all-electric range (AER) combine battery driven electric motors with traditional internal combustion engines in order to reduce emissions emitted to the atmosphere, especially during short, repetitive driving cycles such as commuting to work. A PHEV utilizes grid energy to recharge the electrical energy storage device for use in the AER operation. This study focuses on battery systems as the electrical energy storage device and evaluates commercially available technologies for PHEV through scaled hardware-in-loop (HIL) testing. This project has three main goals: determine the state of technology for PHEV batteries through an extensive literature review, characterize commercially available batteries including simulated HIL response to a real-world PHEV simulation model, and finally, develop a tool to aid in choosing battery types for different vehicle styles (a battery decision matrix). The five different battery types tested are as follows: A123 Lithium Iron Phosphate (LiFePO4) Li-Ion, Genesis Pure Lead-Tin lead acid, generic absorbed glass mat (AGM) valve regulated lead acid (VRLA), SAFT Nickel-Metal Hydride (NiMH) and SAFT Nickel-Cadmium (NiCd). The batteries were characterized in terms of capacity and maximum power as well as tested on an individually scaled real-world duty cycle derived from a model developed by the University of Manitoba and the University of Winnipeg. When comparing the results of the characterization testing with the literature review and manufacturers’ data it was found that there are discrepancies between the batteries tested and the manufacturers’ specifications for mass and capacity. Furthermore, the response to duty cycle testing shows that it is imperative that the internal resistance of the batteries and their conductors should be considered when designing a vehicle, although the literature suggest that this is not commonly done. The results from testing were incorporated into a simple decision matrix factoring in vehicle design constraints, battery performance and cost. Through the duty cycle testing, the dynamic resistance of each of the batteries was determined by measuring the voltage response of the battery to variations in current draw. This resistance figure is important to include in simulations as it effectively reduces available energy the battery can supply due to increasing current demands, as voltage drops in response to a load. / Thesis (Master, Mechanical and Materials Engineering) -- Queen's University, 2011-02-28 15:17:31.209

Hybrid electric vehicles (HEVs) were introduced in response to rising environmental challenges facing the automotive sector. HEVs combine the benefits of electric vehicles and conventional internal combustion engine vehicles, integrating an electrical system (a battery and an electric motor) with an engine to provide improved fuel economy and reduced emissions, while maintaining adequate driving range. By comparison with conventional HEVs, plug-in hybrid electric vehicles (PHEVs) have larger battery storage systems and can be fully charged via an external electric power source such as the electrical grid. Of the three primary PHEV architectures, power-split architectures tend to provide greater efficiencies than parallel or series systems; however, they also demonstrate more complicated dynamics. Thus, in this research project, the problem of optimizing the component sizes of a power-split PHEV was addressed in an effort to exploit the flexibility of this powertrain system and further improve the vehicle's fuel economy, using a Toyota plug-in Prius as the baseline vehicle. Autonomie software was used to develop a vehicle model, which was then applied to formulate an optimization problem for which the main objective is to minimize fuel consumption over standard driving cycles. The design variables considered were: the engine's maximum power, the number of battery cells and the electric motor's maximum power. The genetic algorithm approach was employed to solve the optimization problem for various drive cycles and an acceptable reduction in fuel consumption was achieved thorough the sizing process. The model was validated against a MapleSim model. This research project successfully delivered a framework that integrates an Autonomie PHEV model and genetic algorithm optimization and can be used to address any HEV parameter optimization problem, with any objective, constraints, design variables and optimization parameters.

Increase in energy demand is one of the major challenges that utilities are faced with, thus resulting in an increase in environmental pollution and global warming. The transport sector has a significant share of the energy demand and is a major contribution of emissions to the environment. In Canada, almost 35% of the total energy demand is from the transport sector and it is the second largest source of greenhouse gas (GHG) emissions. The government of Ontario has aimed to move toward a green energy economy, thus resulting in increased penetration of renewable energy sources as well as Plug-in hybrid electric vehicle (PHEV) technology. Penetration of renewable energy sources into microgrids are gradually being recognized as important alternatives in supply side planning. This thesis focuses on the optimal design, planning, sizing and operation of a hybrid, renewable energy based microgrid with the goal of minimizing the lifecycle cost, while taking into account environmental emissions. Four different configurations including a diesel-only, a fully renewable-based, a diesel-renewable mixed, and an external-grid connected microgrid are designed, to compare and evaluate their economics, operational performance and environmental emissions. Analysis is also carried out to determine the break-even economics for a grid-connected microgrid. The well-known energy modeling software for hybrid renewable energy systems, HOMER, is used in the studies reported in this thesis. An optimal power flow (OPF) based optimization framework considering two different objectives, minimizing feeder losses and PHEV charging cost, are presented to understand the impact of PHEV charging on distribution networks. Three different charging periods are considered and the impact of the Ontario Time-of-Use (TOU) tariff on PHEV charging schedules is examined. The impact of PHEV charging on distribution systems in the presence of renewable energy sources is discussed by extending the developed OPF based model to include the contribution of renewable energy sources. The proposed model is evaluated under a variety of scenarios.

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