TRANSPORTATION FOR ELECTRICAL VEHICLES PLAYS A MAJOR ROLE IN THE AUTOMOBILE INDUSTRY

In recent years, people have grown to appreciate using electric vehicles as transportation. According to the circumstances, electric vehicle drives have a number of advantages over ICE cars, mainly in terms of reduced local pollutants, increased energy efficiency, and reduced reliance on oil. However, a number of obstacles, such as limitations in battery technology, high purchasing costs, and therefore a lack of recharging infrastructure, are preventing the quick adoption of electric vehicles. To fully replace ICE cars, EVs must first overcome a few significant challenges. The primary focus of this essay is on some crucial details regarding electric vehicles, such as their many types, electrical equipment, and batteries. This paper's goal is to give information on the existing and future state of electric vehicle technolog

An On-Line Energy Management Strategy for Plug-in Hybrid Electric Vehicles Using an Estimation Distribution Algorithm

DOT 69A3551747114Citation: Xuewei Qi, G. Wu, K. Boriboonsomsin and M. J. Barth, "An on-line energy management strategy for plug-in hybrid electric vehicles using an Estimation Distribution Algorithm," 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, 2014, pp. 2480-2485, doi: 10.1109/ITSC.2014.6958087.In this paper, we propose a generic framework of real-time energy management for PHEVs, where an Estimation Distribution Algorithm (EDA) is used for on-line (i.e., real-time) optimization of the power-split strategy

MODELING OF THERMAL DYNAMICS IN CHEVROLET VOLT GEN II HYBRID ELECTRIC VEHICLE FOR INTEGRATED POWERTRAIN AND HVAC OPTIMAL OPERATION THROUGH CONNECTIVITY

Integrated thermal energy management across system level components in electric vehicles (EVs) and hybrid electric vehicles (HEVs) is currently an under explored space. The proliferation of connected vehicles and accompanying infrastructure in recent years provides additional motivation to explore opportunities in optimizing thermal energy management in EVs and HEVs with the help of this newly available connected vehicle data. This thesis aims to examine and analyze the potential to mitigate vehicle energy consumption and extend usable driving range through optimal control strategies which exploit physical system dynamics via controls integration of vehicle subsystems. In this study, data-driven and physics-based models for heating, ventilation and air-conditioning (HVAC) are developed and utilized along with the vehicle dynamics and powertrain (VD\&PT) models for a GM Chevrolet Volt hybrid electric vehicle to enable co-optimization of HVAC and VD\&PT systems of HEVs. The information available in connected vehicles like driver schedules, trip duration and ambient conditions is leveraged along with the vehicle system dynamics to predict operating conditions of the vehicle under study. All this information is utilized to optimize the operation of an integrated HVAC and VD\&PT system in a hybrid electric vehicle to achieve the goal of reduced energy consumption. For achieving the goals outlined for this thesis, an integrated HVAC and VD\&PT model is developed and the various components, sub-systems and systems are validated against real world test data. Then, integrated relationships relevant to the thermal dynamics of an HEV are established. These relationships comprise the combined operational characteristics of the internal combustion (IC) engine coolant and the cabin electric heater for cabin heating, coordinated controls of IC engine using engine coolant and catalyst temperatures for cabin thermal conditioning in cold ambient conditions and the combined operational characteristics of the air-conditioning compressor for conditioning both cabin and high-voltage battery in an HEV. Next, these sub-system and system relationships are used to evaluate potential energy savings in cabin heating and cooling when vehicle\u27s operating schedule is known. Finally, an optimization study is conducted to establish an energy efficient control strategy which maximizes the HVAC energy efficiency whilst maintaining occupant comfort levels according to ASHRAE standards, all while improving the usable range of the vehicle relative to its baseline calibration. The mean energy savings in overall vehicle energy consumption using an integrated HVAC - Powertrain control strategy and a coordinated thermal management strategy proposed in this work are $3$\% and $14$\% respectively

Connected Hybrid Electrical Vehicle: Powertrain Optimization Strategy and Experiment

University of Minnesota Ph.D. dissertation.August 2017. Major: Mechanical Engineering. Advisor: Zongxuan Sun. 1 computer file (PDF); ix, 101 pages.Power-split Hybrid Electric Vehicle (HEV), which accounts for almost 40% of US hybrid-car total sales in 2013, has the ability to store excess energy during driving and braking, and to split the demanded power between the engine and battery. With the advent of connected vehicles, traffic information can be shared and utilized to further optimize HEV’s energy use, by predicting the demanded power and optimizing the power-split. However, traffic conditions, and therefore the demanded power, are constantly changing. As a result, the optimization method not only has to account for optimality and charge-sustaining conditions, but also driving-cycle sensitivity and speed of calculation for real-time implementation. This research therefore proposes fast HEV powertrain optimization to improve fuel economy for connected vehicle applications. Additionally, in order to measure the performance of connected vehicle applications, a hardware-in-the-loop system (HiLS), that combines an existing laboratory powertrain research platform with a microscopic traffic simulator, is developed. A computationally-efficient analytical solution to the HEV powertrain optimization problem utilizing vehicle speed prediction based on Inter-Vehicle Communications and Vehicle-Infrastructure Integration is proposed for real-time implementation. First, Gipps’ car following model for traffic prediction is used to predict the interactions between vehicles, combined with the cell-transmission-model for the leading vehicle trajectory prediction. Secondly, a computationally efficient charge-sustaining (CS) HEV powertrain optimization strategy is analytically derived and simulated, based on the Pontryagin’s Minimum Principle (PMP) and a CS-condition constraint. A 3D lookup-map, generated offline to interpolate the optimizing parameters based on the predicted speed, is also utilized to speed up the calculations. Simulations are conducted for 6-mile and 15-mile cases with different prediction update timings to test the performance of the proposed strategy against a Rule-Based (RB) control strategy on a Toyota Prius engine. Results for accurate-prediction cases show 9.6% average fuel economy improvements in miles-per-gallon (MPG) over RB for the 6-mile case and 7% improvements for the 15-mile case. Prediction-with-error cases show smaller average MPG’s improvements, with 1.6% to 4.3% improvements for the 6-mile case and 2.6% to 3.4% improvements for the 15-mile case. For practical purposes, the HEV engine operating range and transient response have to be considered, which introduces additional optimization constraints. Solving a nonlinear optimization problem with constraints analytically is difficult, while numerically is computational heavy and time consuming. Therefore, the nonlinear HEV optimization problem with constraints is expressed and solved as a Separable Programming (SP) problem. First, given the flexibility of the power-split HEV powertrain, the relationship between the minimum fuel consumption and the power-split levels between the engine and battery is calculated and stored offline for all possible vehicle power demands. Therefore, the relationship between HEV power-split levels and engine operating points with minimum fuel consumption for a given vehicle power demand is obtained. Secondly, the problem is formulated with fuel consumption as the cost and power-split level as the optimizing input and solved using SP. In SP, the nonlinear fuel cost and battery charging rate relationships with the power-split levels are approximated as linear-piecewise functions which introduce dimensionless variables that are linear to the input and outputs of the nonlinear functions. The input range constraint and the engine transient dynamics are also formulated. The optimization problem is then solved as a large-dimension linear problem with linear constraints using efficient Linear Programming solvers. The proposed optimization method is then simulated in a receding horizon fashion with various vehicle speed profiles and a case study was tested on a real John Deere diesel engine. Comparable fuel economy with Dynamic Programming is shown with significantly less calculation time and fuel savings of 4.0% and 10.4% over PMP and RB optimizations are observed. A HiLS testbed to evaluate the performance of connected vehicle applications is proposed. A laboratory powertrain research platform, which consists of a real engine, an engine-loading device (hydrostatic dynamometer) and a virtual powertrain model to represent a vehicle, is connected remotely to a microscopic traffic simulator (VISSIM). Vehicle dynamics and road conditions of a target vehicle in the VISSIM simulation are transmitted to the powertrain research platform through the internet, where the power demand can then be calculated. The engine then operates through an engine optimization procedure to minimize fuel consumption, while the dynamometer tracks the desired engine load based on the target vehicle information. Test results show fast data transfer at every 200 milliseconds and good tracking of the optimized engine operating points and the desired vehicle speed. Actual fuel and emissions measurements, which otherwise could not be calculated precisely by fuel and emission maps in simulations, are achieved by the testbed. In addition, VISSIM simulation can be implemented remotely while connected to the powertrain research platform through the internet, allowing easy access to the laboratory setup

Stratégies de gestion d’énergie pour véhicules électriques et hybride avec systèmes hybride de stockage d’énergie

Les véhicules électriques et hybrides font partie des éléments clés pour résoudre les problèmes de réchauffement de la planète et d'épuisement des ressources en combustibles fossiles dans le domaine du transporte. En raison des limites des différents systèmes de stockage et de conversion d’énergie en termes de puissance et d'énergie, les hybridations sont intéressantes pour les véhicules électriques (VE). Dans cette thèse, deux hybridations typiques sont étudiées • un sous-système de stockage d'énergie hybride combinant des batteries et des supercondensateurs (SC) ; • et un sous-système de traction hybride parallèle combinant moteur à combustion interne et entraînement électrique. Ces sources d'énergie et ces conversions combinées doivent être gérées dans le cadre de stratégies de gestion de l'énergie (SGE). Parmi celles-ci, les méthodes basées sur l'optimisation présentent un intérêt en raison de leur approche systématique et de leurs performances élevées. Néanmoins, ces méthodes sont souvent compliquées et demandent beaucoup de temps de calcul, ce qui peut être difficile à réaliser dans des applications réelles. L'objectif de cette thèse est de développer des SGE simples mais efficaces basées sur l'optimisation en temps réel pour un VE et un camion à traction hybride parallèle alimentés par des batteries et des SC (système de stockage hybride). Les complexités du système étudié sont réduites en utilisant la représentation macroscopique énergétique (REM). La REM permet de réaliser des modèles réduits pour la gestion de l'énergie au niveau de la supervision. La théorie du contrôle optimal est ensuite appliquée à ces modèles réduits pour réaliser des SGE en temps réel. Ces stratégies sont basées sur des réductions de modèle appropriées, mais elles sont systématiques et performantes. Les performances des SGE proposées sont vérifiées en simulation par comparaison avec l’optimum théorique (programmation dynamique). De plus, les capacités en temps réel des SGE développées sont validées via des expériences en « hardware-in-the-loop » à puissances réduites. Les résultats confirment les avantages des stratégies proposées développées par l'approche unifiée de la thèse.Abstract: Electric and hybrid vehicles are among the keys to solve the problems of global warming and exhausted fossil fuel resources in transportation sector. Due to the limits of energy sources and energy converters in terms of power and energy, hybridizations are of interest for future electrified vehicles. Two typical hybridizations are studied in this thesis: • hybrid energy storage subsystem combining batteries and supercapacitors (SCs); and • hybrid traction subsystem combining internal combustion engine and electric drive. Such combined energy sources and converters must be handled by energy management strategies (EMSs). In which, optimization-based methods are of interest due to their high performance. Nonetheless, these methods are often complicated and computation consuming which can be difficult to be realized in real-world applications. The objective of this thesis is to develop simple but effective real-time optimization-based EMSs for an electric car and a parallel hybrid truck supplied by batteries and SCs. The complexities of the studied system are tackled by using Energetic Macroscopic Representation (EMR) which helps to conduct reduced models for energy management at the supervisory level. Optimal control theory is then applied to these reduced models to accomplish real-time EMSs. These strategies are simple due to the suitable model reductions but systematic and high-performance due to the optimization-based methods. The performances of the proposed strategies are verified via simulations by comparing with off-line optimal benchmark deduced by dynamic programming. Moreover, real-time capabilities of these novel EMSs are validated via experiments by using reduced-scale power hardware-in-the-loop simulation. The results confirm the advantages of the proposed strategies developed by the unified approach in the thesis

Estratégias de Operação para Sistemas Fotovoltaicos com Armazenamento Híbrido de Energia Elétrica

Na sociedade atual a energia elétrica é considerada um bem essencial para a vida de cada indivíduo. De modo a acompanhar esta crescente tendência na utilização de energia elétrica, surge cada vez mais uma implícita responsabilidade da sociedade na procura de fontes de energia mais limpas. Uma das mais atrativas soluções neste momento passa pela produção fotovoltaica. A utilização desse tipo de energia, obtida por meio da transformação direta de recursos naturais, é atualmente estudada com grande interesse pela comunidade científica, devido à sua complexidade, tanto pelas diferentes fontes de produção, quanto pela sua variabilidade e imprevisibilidade. No entanto, essa falta de previsibilidade poderia ser compensada pela complementaridade entre recursos ou pela introdução de sistemas de armazenamento de energia elétrica. Os sistemas de armazenamento de energia elétrica são reconhecidos como uma das abordagens mais promissoras. No entanto, estes sistemas sofrem de alguns problemas operacionais, como por exemplo a degradação do desempenho quando sujeitos a altas correntes de carga / descarga e uma consequente redução da sua vida útil. Para mitigar estas desvantagens, começaram a surgir os sistemas de armazenamento híbridos de energia. Estes sistemas combinam benefícios de duas ou mais tecnologias diferentes. A ligação de super-condensadores e baterias de Li-ion, que combina a alta densidade de potência de super-condensadores com a alta densidade energética das baterias de Li-ion, é a topologia mais usual neste tipo de sistema. Esta dissertação tem como objetivo o dimensionamento, construção e controlo de um sistema isolado da rede elétrica, com extração de energia fotovoltaica e armazenamento híbrido de energia elétrica.In today's society, electricity is considered an essential asset for the life of each individual. In order to keep up with this growing trend, in the use of electric energy in previously manually performed tasks, there is an increasing implicit responsibility of society to seek cleaner sources of energy. One of the most attractive solutions, at this moment, is photovoltaic production. The use of this type of energy, obtained through the direct transformation of natural resources, is currently studied with great interest by the scientific community, due to its complexity, as well as its variability and unpredictability. However, this lack of predictability can be eliminated by the complementarity between resources or by the introduction of electrical energy storage systems. Energy storage systems are recognized as one of the most promising approaches. However, these systems suffer from some operational problems, such as degradation of performance when subjected to high load/discharge currents and a consequent reduction in their useful life. To mitigate these drawbacks, hybrid power storage systems began to emerge. These systems combine the benefits of two or more different technologies. The connection of super capacitors and Li-ion cells, thus combining the high power density of super capacitors with the high energy density of Li-ion cells, is the most common topology in this type of system. This dissertation aims at the design, construction and control of an isolated grid system, with extraction of photovoltaic energy and hybrid storage of electric energy

Achieving Sustainability in Urban Transit and High-occupancy Vehicle Systems Through Emerging Technologies and Operations

It is critical to reduce road transportation emissions, as it is the major source contributing to greenhouse gas emissions (GHGs) and air pollution. However, the increasing travel demand due to the rise of population and economy makes the task challenging. Mathematically, road transportation emissions can be minimized in the manner of reducing total vehicle miles traveled (VMT) and lowering vehicle emission rate. Many strategies have been proven viable for achieving transportation sustainability. For example, the total VMT can be reduced by switching low-occupancy transportation modes (i.e., driving alone) to high-occupancy transportation modes (i.e., public transit, ridesharing). The development of alternative-fuel vehicles (i.e., hybrid vehicles and electric vehicles) provides the prospect of minimizing vehicle emission rates or achieving net zero emissions. These strategies are active in urban transit and high-occupancy vehicle systems. Many studies focused on the field of urban transit and high-occupancy vehicle systems. However, there are several challenges and scientific gaps related to sustainability in this field. For example, the lack of investigation of energy estimation models for hybrid buses that can support energy-oriented transit operations. The need to develop an environmental impact evaluation framework for assess the energy and environmental impact of ridesharing services with consideration of individual level (i.e., agent) behavior changes. An energy-saving and time efficient charging system for electric buses on complex transit networks is required to developed in order to promote the emerging of electric buses in transit usage