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.

The main goal of this thesis study is the optimization of the basic design parameters of hybrid electric vehicle drivetrain components to minimize fuel consumption and emission objectives, together with constraints derived from performance requirements. In order to generate a user friendly and flexible platform to model, select drivetrain components, simulate performance, and optimize parameters of series and parallel hybrid electric vehicles, a MATLAB based graphical user interface is designed. A basic sizing procedure for the internal combustion engine, electric motor, and battery is developed. Pre-defined control strategies are implemented for both types of hybrid configurations. To achieve better fuel consumption and emission values, while satisfying nonlinear performance constraints, multi-objective gradient based optimization procedure is carried out with user defined upper and lower bounds of optimization parameters. The optimization process is applied to a number of case studies and the results are evaluated by comparison with similar cases found in literature.

Indiana University-Purdue University Indianapolis (IUPUI) / Today transportation is one of the rapidly evolving technologies in the world. With the stringent mandatory emission regulations and high fuel prices, researchers and manufacturers are ever increasingly pushed to the frontiers of research in pursuit of alternative propulsion systems. Electrically propelled vehicles are one of the most promising solutions among all the other alternatives, as far as; reliability, availability, feasibility and safety issues are concerned. However, the shortcomings of a fully electric vehicle in fulfilling all performance requirements make the electrification of the conventional engine powered vehicles in the form of a plug-in hybrid electric vehicle (PHEV) the most feasible propulsion systems. The optimal combination of the properly sized components such as internal combustion engine, electric motor, energy storage unit are crucial for the vehicle to meet the performance requirements, improve fuel efficiency, reduce emissions, and cost effectiveness. In this thesis an application of Particle Swarm Optimization (PSO) approach to optimally size the vehicle powertrain components (e.g. engine power, electric motor power, and battery energy capacity) while meeting all the critical performance requirements, such as acceleration, grade and maximum speed is studied. Compared to conventional optimization methods, PSO handles the nonlinear constrained optimization problems more efficiently and precisely. The PHEV powertrain configuration with the determined sizes of the components has been used in a new vehicle model in PSAT (Powertrain System Analysis Toolkit) platform. The simulation results show that with the optimized component sizes of the PHEV vehicle (via PSO), the performance and the fuel efficiency of the vehicle are significantly improved. The optimal solution of the component sizes found in this research increased the performance and the fuel efficiency of the vehicle. Furthermore, after reaching the desired values of the component sizes that meet all the performance requirements, the overall emission of hazardous pollutants from the PHEV powertrain is included in the optimization problem in order to obtain updated PHEV component sizes that would also meet additional design specifications and requirements.

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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

In this research, the focus is on improving a designer's capability to determine near-optimal sizes of components for a given system architecture. Component sizing is a hard problem to solve because of the presence of competing objectives, requirements from multiple disciplines, and the need for finding a solution quickly for the architecture being considered. In current approaches, designers rely on heuristics and iterate over the multiple objectives and requirements until a satisfactory solution is found. To improve on this state of practice, this research introduces advances in the following two areas: a.) Formulating a component sizing problem in a manner that is convenient to designers and b.) Solving the component sizing problem in an efficient manner so that all of the imposed requirements are satisfied simultaneously and the solution obtained is mathematically optimal. In particular, an acausal, algebraic, equation-based, declarative modeling approach is taken to solve component sizing problems efficiently. This is because global optimization algorithms exist for algebraic models and the computation time is considerably less as compared to the optimization of dynamic simulations. In this thesis, the mathematical programming language known as GAMS (General Algebraic Modeling System) and its associated global optimization solvers are used to solve component sizing problems efficiently. Mathematical programming languages such as GAMS are not convenient for formulating component sizing problems and therefore the Systems Modeling Language developed by the Object Management Group (OMG SysML ) is used to formally capture and organize models related to component sizing into libraries that can be reused to compose new models quickly by connecting them together. Model-transformations are then used to generate low-level mathematical programming models in GAMS that can be solved using commercial off-the-shelf solvers such as BARON (Branch and Reduce Optimization Navigator) to determine the component sizes that satisfy the requirements and objectives imposed on the system. This framework is illustrated by applying it to an example application for sizing a hydraulic log splitter.

En av de största utmaningarna framöver är att säkerställa en jämn övergång från fossila bränslen till förnybar energi utan att kompromissa driftstabilitet. För att genomföra detta behövs det någon form av energilagring för att lagra överskott av förnybar energi, exempelvis solenergi, som sedan kan användas när solenergin inte räcker till för att täcka elbehovet. Syftet med detta projektet är att modellera ett fristående mikronät med vätgaslagring i Simulink. Empiriska data har samlats för samtliga komponenter, som inkluderar en PEM bränslecell, ett NMC622 litiumjonbatteri och en AEM elektrolysör. På så sätt kunde ideala driftparametrar identifieras för komponenterna. Det modellerade systemet klarade elbehovet av en 2 W lampa för en hel vinter-, vår- och sommardag. Genom att använda väderdata för en vårdag kunde systemet dimensioneras för att uppfylla kravet för neutral balans med avseende på vätgasnivån och batteriernas laddningstillstånd. Det krävdes 38 solceller (med en total nominell effekt på 37.95 W), 5 batterier (med en total kapacitet av 175 mAh, 2 bränsleceller (med en vald effekt på 2.34 W inom det ohmiska intervallet) och 2 elektrolysörer (med en maximal effekt på 14.8504 W) för att uppfylla kravet. För att mikronätsystemet ska ha en större ekonomisk potential och en bättre genomförbarhet bör simuleringen förbättras genom att inkludera fler parametrar och använda väderdata som täcker längre perioder.