Orientador: Franco Giuseppe Dedini / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Mecânica / Made available in DSpace on 2018-08-23T05:45:50Z (GMT). No. of bitstreams: 1 Correa_FernandaCristina_D.pdf: 2997267 bytes, checksum: d232b968b2a0c38ad71028200424345b (MD5) Previous issue date: 2013 / Resumo: O considerável aumento de automóveis tem causado graves efeitos ao meio ambiente e para o homem, fato este que incentiva a pesquisa pela obtenção de formas de energia mais limpa. A adoção da tecnologia propulsora híbrida veicular tem contribuído para a redução da agressão causada pelos automóveis ao meio ambiente. O termo "híbrido" deriva da combinação de duas ou mais fontes de potência, sendo que a combinação mais comum se faz através de um motor de combustão interna (MCI), comumente usado em veículos convencionais, com o conjunto bateria e motor elétrico (ME) usados nos VEs (Veículos Elétricos). Em geral, a principal razão para usar a arquitetura híbrida elétrica é o grau de liberdade adicional devido à presença de uma fonte de energia adicional além do tanque de combustível; isto implica que, a cada instante, a potência necessária pelo veículo pode ser fornecida por uma dessas fontes, ou por uma combinação das duas. Escolher a combinação correta é normalmente uma tarefa complexa. Dentro desse contexto, neste trabalho é realizado o desenvolvimento e a análise das estratégias de gerenciamento de potência em um VEH a fim de minimizar o seu consumo de combustível e consequentemente suas emissões. São desenvolvidas duas estratégias de gerenciamento, a primeira baseada em regras e a segunda utilizando sistemas fuzzy. Além disso, também é realizado o controle interno do motor a combustão, de forma que este opere, na maior do tempo, em regiões que tenham um menor consumo específico. Por meio da utilização das estratégias de gerenciamento de potência aliadas ao controle do motor a combustão é possível obter uma considerável economia de combustível. Comparando-se as duas estratégias desenvolvidas, a estratégia baseada em fuzzy é a que proporcionou melhor economia de combustível / Abstract: The considerable increase of automobiles has caused serious effects to the environment and to man; this fact encourages research by obtaining forms of cleaner energy. The adoption of hybrid vehicle propulsion technology has contributed to the reduction of aggression caused by car to the environment. The term "hybrid" derives from the combination of two or more power sources, and the most common combination is by of the engine (MCI), commonly used in conventional vehicles, together with the battery and motor electric (ME) used in EVs (Electric Vehicles). In general, the main reason to use the architecture electric hybrid is the additional degree of freedom due to the presence of an energy source plus additional fuel tank, this implies that, at each instant, the power required the vehicle may be provided by one of these sources, or a combination of both. Choose the correct combination is usually a complex task. Within of this context, this work development is undertaken and analysis of strategies for power management in a VEH in order to minimize fuel consumption and thus emissions. Two management strategies are developed, the first is based in rules and the second is based in fuzzy systems. Furthermore, it is also performed internal control engine combustion of so that it operates at greater time in regions having lower specific fuel consumption. Through the use of power management strategies coupled with the motor control combustion is possible to obtain a considerable saving of fuel. Comparing the two strategies developed, the strategy based on fuzzy is that provided better economy fuel / Doutorado / Mecanica dos Sólidos e Projeto Mecanico / Doutora em Engenharia Mecânica

M.Ing. / The increase in awareness of the environmental problems resulting from emissions released from vehicles have forced governments and car manufactures to invest more time in to the designing a vehicle that is an alternative to petrol driven vehicles. This dissertation aims to introduce a control strategy to manage the flow of energy of different power sources that may be found on a vehicle. Hybrid vehicles are a possible solution to reducing carbon emissions that play a part in global warming. In this dissertation, di erent hybrid vehicles are de ned and their components discussed in detail. The possibility of more than one energy source to power the vehicle introduces more exibility in terms of the drivetrain but this increases the complexity of the energy control management. The goal is to optimize the energy control management to reduce fuel consumption and therefore reduce emissions. Operating procedures for the various hybrid con gurations are discussed. Simulations of the Energy Management System of the hybrid electric vehicle are used to develop the control optimization algorithm. Various control optimization procedures are discussed. Satisfactory results from the simulations allow the implementation of the hybrid onto a platform entered into the South African Solar Challenge 2010, which covered a distance of 4000 km. The Energy Management system selected for the parallel hybrid electric vehicle demonstrated fuel savings, which meant a reduction in emissions, which is the goal of any hybrid vehicle. Further investigations include more intelligent controllers to adjust the parameters of the energy management controller to allow for adaptation to various driving conditions, e.g. urban and motorway driving.

Connected and Autonomous Vehicles (CAVs) benefit from both connectivity between vehicles and city infrastructures and automation of vehicles. In this respect, CAVs can improve safety and reduce traffic congestion and environmental impacts of daily commutes through making collaborative decisions. This dissertation studies how to reduce the energy consumption of vehicles and traffic congestion by making high-level routing decisions of CAVs. The first half of this dissertation considers the problem of eco-routing (finding the energy-optimal route) for Plug-In Hybrid Electric Vehicles (PHEVs) to minimize the overall energy consumption cost. Several algorithms are proposed that can simultaneously calculate an energy-optimal route (eco-route) for a PHEV and an optimal power-train control strategy over this route. The results show significant energy savings for PHEVs with a near real-time execution time for the algorithms. The second half of this dissertation tackles the problem of routing for fleets of CAVs in the presence of mixed traffic (coexistence of regular vehicles and CAVs). In this setting, all CAVs belong to the same fleet and can be routed using a centralized controller. The routing objective is to minimize a given overall fleet traveling cost (travel time or energy consumption). It is assumed that regular vehicles (non-CAVs) choose their routing decisions selfishly to minimize their traveling time. A framework is proposed that deals with the routing interaction between CAVs and regular uncontrolled vehicles under different penetration rates (fractions) of CAVs. The results suggest collaborative routing decisions of CAVs improve not only the cost of CAVs but also that of the non-CAVs. This framework is further extended to consider congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. A network flow model is devised to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. The results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.

Indiana University-Purdue University Indianapolis (IUPUI) / An increase in the awareness of environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with the aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play a vital role in improving the efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed, and SOC trajectory, as 50 element time-variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for a given drive cycle. Observations of the trade-off are discussed in the case of Multi-Objective Optimizations.

Indiana University-Purdue University Indianapolis (IUPUI) / This thesis proposes an Adaptive Rule-Based Energy Management Strategy (ARBS EMS) for a parallel hybrid electric vehicle (P-HEV). The strategy can effciently be deployed online without the need for complete knowledge of the entire duty cycle in order to optimize fuel consumption. ARBS improves upon the established Preliminary Rule-Based Strategy (PRBS) which has been adopted in commercial vehicles. When compared to PRBS, the aim of ARBS is to maintain the battery State of Charge (SOC) which ensures the availability of the battery over extended distances. The proposed strategy prevents the engine from operating in highly ineffcient regions and reduces the total equivalent fuel consumption of the vehicle. Using an HEV model developed in Simulink, both the proposed ARBS and the established PRBS strategies are compared across eight short duty cycles and one long duty cycle with urban and highway characteristics. Compared to PRBS, the results show that, on average, a 1.19% improvement in the miles per gallon equivalent (MPGe) is obtained with ARBS when the battery initial SOC is 63% for short duty cycles. However, as opposed to PRBS, ARBS has the advantage of not requiring any prior knowledge of the engine effciency maps in order to achieve optimal performance. This characteristics can help in the systematic aftermarket hybridization of heavy duty vehicles.

This thesis focuses on developing an intelligent energy management strategy for eco-driving in Connected and Autonomous Hybrid Electric Vehicles (CA-HEV's), which can be implemented in real-time. The strategy is divided into two layers, i.e. the upper level controller and the lower level controller. The upper level controller can be executed on the remote server. It is responsible for extracting the information from the driver about the trip and the vehicle information using the communication capabilities of the CA-HEV. The gathered information is then utilized by dynamic programming (DP), which is implemented in a bi-layer fashion to reduce the computation burden on the server. The outer layer of the DP algorithm and the optimal velocity trajectory and the inner layer optimizes the power distribution in the powertrain to minimize fuel consumption alongside maintaining charge balance conditions. These global optimal results are evaluated for an ideal environment without any traffic information. The lower level controller is responsible for real-time implementation on vehicles in the real world environment and is based on a well-accredited reinforcement learning (RL) strategy, i.e., Q-learning. The RL-based controller optimally distributes the power in a CA-HEV and maintains charge balance conditions. Furthermore, the RL-based controller is also trained on the remote server based on global optimal results obtained from the DP algorithm. The optimal parameter information is then resent to the vehicle's embedded controller for real-time implementation. Simulations are performed for Toyata Prius (2010) on MATLAB and Simulink, and road information is gathered from SUMO. Simulation results provide a comparative study between the global optimal and the RL-based controller. To validate the adaptiveness of the RL-based controller, it is also tested on two approximate real-world drivecycles and its performance is compared against global optimal results evaluated using DP. / Thesis / Master of Applied Science (MASc)

The rising use of fossil fuels and the resulting rise in environmental harm have fueled the advancement of automobiles that are fuel-efficient. A severe existential challenge facing the planet earth has given rise to hybrid electric vehicles (HEVs), which have developed from their incipient stage and are shown promise as a solution. Additionally, when needed to produce peaking power, batteries' efficiency is reduced. Instead, supercapacitors have smaller energy storage capacity but can withstand peaking power. Designing a clever method to manage the energy balance between a supercapacitor and a battery is the main goal of this research. Different topologies are used to study the battery-supercapacitor energy storage system in great detail. Nitrogen oxides (NOx), carbon monoxide (CO), hydrocarbons (HC), and other harmful gases are less released when a battery-supercapacitor energy storage system is integrated. Additionally, it can lower the load on the battery, extending its life and improving its performance in HEVs.

Indiana University-Purdue University Indianapolis (IUPUI) / Plug-in Hybrid Electric Vehicles (PHEV) are new generation Hybrid Electric Vehicles (HEV) with larger battery capacity compared to Hybrid Electric Vehicles. They can store electrical energy from a domestic power supply and can drive the vehicle alone in Electric Vehicle (EV) mode. According to the U.S. Department of Transportation 80 % of the American driving public on average drives under 50 miles per day. A PHEV vehicle that can drive up to 50 miles by making maximum use of cheaper electrical energy from a domestic supply can significantly reduce the conventional fuel consumption. This may also help in improving the environment as PHEVs emit less harmful gases. However, the Energy Management System (EMS) of PHEVs would have to be very different from existing EMSs of HEVs. In this thesis, three different Energy Management Systems have been designed specifically for PHEVs using simulated study. For most of the EMS development mathematical vehicle models for powersplit drivetrain configuration are built and later on the results are tested on advanced vehicle modeling tools like ADVISOR or PSAT. The main objective of the study is to design EMSs to reduce fuel consumption by the vehicle. These EMSs are compared with existing EMSs which show overall improvement. x In this thesis the final EMS is designed in three intermediate steps. First, a simple rule based EMS was designed to improve the fuel economy for parametric study. Second, an optimized EMS was designed with the main objective to improve fuel economy of the vehicle. Here Particle Swarm Optimization (PSO) technique is used to obtain the optimum parameter values. This EMS has provided optimum parameters which result in optimum blended mode operation of the vehicle. Finally, to obtain optimum charge depletion and charge sustaining mode operation of the vehicle an advanced PSO EMS is designed which provides optimal results for the vehicle to operate in charge depletion and charge sustaining modes. Furthermore, to implement the developed advanced PSO EMS in real-time a possible real time implementation technique is designed using neural networks. This neural network implementation provides sub-optimal results as compared to advanced PSO EMS results but it can be implemented in real time in a vehicle. These EMSs can be used to obtain optimal results for the vehicle driving conditions such that fuel economy is improved. Moreover, the optimal designed EMS can also be implemented in real-time using the neural network procedure described.

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.

This work presents novel modeling and experimental techniques that provide insight into liquid-phase mass transport and electron transfer processes in lithium-ion batteries. These included liquid-phase ionic mass transport (conduction and diffusion), lithium diffuion in the solid phase and electronic transport in the solid phase. Fundamental understanding of these processes is necessary to efficiently design and optimize lithium-ion batteries for different applications. To understand the effect of electrode structure on the electronic resistance of the cathode, we tested power performance of cathodes with combinations of three different carbon conductivity additives: vapor-grown carbon fibers (CF), carbon black (CB) and graphite (GR). With all other factors held constant, cathodes with a mixture of CF+CB were found to have the best power-performance, followed by cells containing CF only and then by CB+GR. Thus, the use of carbon fibers as conductive additive was found to improve the power performance of cells compared to the baseline (CB+GR). The enhanced electrode performance due to the fibers also allows an increase in energy density while still meeting power goals. About one-third of the available energy was lost to irreversible processes when cells were pulse-charged or discharged at the maximum rate allowed by voltage-cutoff constraints. We developed modeling and experimental techniques to quantify tortuosity in electrolyte-filled porous battery structures (separator and active-material film). Tortuosities of separators were measured by two methods, AC impedance and polarization-interrupt, which produced consistent results. The polarization-interrupt experiment was used similarly to measure effective electrolyte transport in porous films of cathode materials, particularly films containing lithium iron phosphate. An empirical relationship between porosity and the tortuosity of the porous structures was developed. Our results demonstrate that the tortuosity-dependent mass transport resistance in porous separators and electrodes is significantly higher than that predicted by the oft-used Bruggeman relationship. To understand the dominant resistances in a lithium battery, we developed and validated a model for lithium iron phosphate cathode. In doing so we considered unique physical features of this active material. Our model is unusual in terms of the range of experimental conditions for which it is validated. Various submodel and experimental techniques were used to find physically realistic parameters. The model was tested with different discharge rates and thicknesses of cathodes, in all cases showing good agreement, which suggests that the model takes into account physical realities with different thicknesses. The model was then used to find the dominant resistance for the tested cathodes. The model suggests that the inter-particle contact resistance between carbon and the active-material particles was a dominant resistance for the tested cathodes.