Detection of Communities within the Multibody System Dynamics Network and Analysis of Their Relations

Multibody system dynamics is already a well developed branch of theoretical, computational and applied mechanics. Thousands of documents can be found in any of the well-known scientific databases. In this work it is demonstrated that multibody system dynamics is built of many thematic communities. Using the Elsevier’s abstract and citation database SCOPUS, a massive amount of data is collected and analyzed with the use of the open source visualization tool Gephi. The information is represented as a large set of nodes with connections to study their graphical distribution and explore geometry and symmetries. A randomized radial symmetry is found in the graphical representation of the collected information. Furthermore, the concept of modularity is used to demonstrate that community structures are present in the field of multibody system dynamics. In particular, twenty-four different thematic communities have been identified. The scientific production of each community is analyzed, which allows to predict its growing rate in the next years. The journals and conference proceedings mainly used by the authors belonging to the community as well as the cooperation between them by country are also analyzed

Optimal Control for Automotive Powertrain Applications

Optimal Control (OC) is essentially a mathematical extremal problem. The procedure consists on the definition of a criterion to minimize (or maximize), some constraints that must be fulfilled and boundary conditions or disturbances affecting to the system behavior. The OC theory supplies methods to derive a control trajectory that minimizes (or maximizes) that criterion. This dissertation addresses the application of OC to automotive control problems at the powertrain level, with emphasis on the internal combustion engine. The necessary tools are an optimization method and a mathematical representation of the powertrain. Thus, the OC theory is reviewed with a quantitative analysis of the advantages and drawbacks of the three optimization methods available in literature: dynamic programming, Pontryagin minimum principle and direct methods. Implementation algorithms for these three methods are developed and described in detail. In addition to that, an experimentally validated dynamic powertrain model is developed, comprising longitudinal vehicle dynamics, electrical motor and battery models, and a mean value engine model. OC can be utilized for three different purposes: 1. Applied control, when all boundaries can be accurately defined. The engine control is addressed with this approach assuming that a the driving cycle is known in advance, translating into a large mathematical problem. Two specific cases are studied: the management of a dual-loop EGR system, and the full control of engine actuators, namely fueling rate, SOI, EGR and VGT settings. 2. Derivation of near-optimal control rules, to be used if some disturbances are unknown. In this context, cycle-specific engine calibrations calculation, and a stochastic feedback control for power-split management in hybrid vehicles are analyzed. 3. Use of OC trajectories as a benchmark or base line to improve the system design and efficiency with an objective criterion. OC is used to optimize the heat release law of a diesel engine and to size a hybrid powertrain with a further cost analysis. OC strategies have been applied experimentally in the works related to the internal combustion engine, showing significant improvements but non-negligible difficulties, which are analyzed and discussed. The methods developed in this dissertation are general and can be extended to other criteria if appropriate models are available.El Control Óptimo (CO) es esencialmente un problema matemático de búsqueda de extremos, consistente en la definición de un criterio a minimizar (o maximizar), restricciones que deben satisfacerse y condiciones de contorno que afectan al sistema. La teoría de CO ofrece métodos para derivar una trayectoria de control que minimiza (o maximiza) ese criterio. Esta Tesis trata la aplicación del CO en automoción, y especialmente en el motor de combustión interna. Las herramientas necesarias son un método de optimización y una representación matemática de la planta motriz. Para ello, se realiza un análisis cuantitativo de las ventajas e inconvenientes de los tres métodos de optimización existentes en la literatura: programación dinámica, principio mínimo de Pontryagin y métodos directos. Se desarrollan y describen los algoritmos para implementar estos métodos así como un modelo de planta motriz, validado experimentalmente, que incluye la dinámica longitudinal del vehículo, modelos para el motor eléctrico y las baterías, y un modelo de motor de combustión de valores medios. El CO puede utilizarse para tres objetivos distintos: 1. Control aplicado, en caso de que las condiciones de contorno estén definidas. Puede aplicarse al control del motor de combustión para un ciclo de conducción dado, traduciéndose en un problema matemático de grandes dimensiones. Se estudian dos casos particulares: la gestión de un sistema de EGR de doble lazo, y el control completo del motor, en particular de las consignas de inyección, SOI, EGR y VGT. 2. Obtención de reglas de control cuasi-óptimas, aplicables en casos en los que no todas las perturbaciones se conocen. A este respecto, se analizan el cálculo de calibraciones de motor específicas para un ciclo, y la gestión energética de un vehículo híbrido mediante un control estocástico en bucle cerrado. 3. Empleo de trayectorias de CO como comparativa o referencia para tareas de diseño y mejora, ofreciendo un criterio objetivo. La ley de combustión así como el dimensionado de una planta motriz híbrida se optimizan mediante el uso de CO. Las estrategias de CO han sido aplicadas experimentalmente en los trabajos referentes al motor de combustión, poniendo de manifiesto sus ventajas sustanciales, pero también analizando dificultades y líneas de actuación para superarlas. Los métodos desarrollados en esta Tesis Doctoral son generales y aplicables a otros criterios si se dispone de los modelos adecuados.El Control Òptim (CO) és essencialment un problema matemàtic de cerca d'extrems, que consisteix en la definició d'un criteri a minimitzar (o maximitzar), restriccions que es deuen satisfer i condicions de contorn que afecten el sistema. La teoria de CO ofereix mètodes per a derivar una trajectòria de control que minimitza (o maximitza) aquest criteri. Aquesta Tesi tracta l'aplicació del CO en automoció i especialment al motor de combustió interna. Les ferramentes necessàries són un mètode d'optimització i una representació matemàtica de la planta motriu. Per a això, es realitza una anàlisi quantitatiu dels avantatges i inconvenients dels tres mètodes d'optimització existents a la literatura: programació dinàmica, principi mínim de Pontryagin i mètodes directes. Es desenvolupen i descriuen els algoritmes per a implementar aquests mètodes així com un model de planta motriu, validat experimentalment, que inclou la dinàmica longitudinal del vehicle, models per al motor elèctric i les bateries, i un model de motor de combustió de valors mitjans. El CO es pot utilitzar per a tres objectius diferents: 1. Control aplicat, en cas que les condicions de contorn estiguen definides. Es pot aplicar al control del motor de combustió per a un cicle de conducció particular, traduint-se en un problema matemàtic de grans dimensions. S'estudien dos casos particulars: la gestió d'un sistema d'EGR de doble llaç, i el control complet del motor, particularment de les consignes d'injecció, SOI, EGR i VGT. 2. Obtenció de regles de control quasi-òptimes, aplicables als casos on no totes les pertorbacions són conegudes. A aquest respecte, s'analitzen el càlcul de calibratges específics de motor per a un cicle, i la gestió energètica d'un vehicle híbrid mitjançant un control estocàstic en bucle tancat. 3. Utilització de trajectòries de CO com comparativa o referència per a tasques de disseny i millora, oferint un criteri objectiu. La llei de combustió així com el dimensionament d'una planta motriu híbrida s'optimitzen mitjançant l'ús de CO. Les estratègies de CO han sigut aplicades experimentalment als treballs referents al motor de combustió, manifestant els seus substancials avantatges, però també analitzant dificultats i línies d'actuació per superar-les. Els mètodes desenvolupats a aquesta Tesi Doctoral són generals i aplicables a uns altres criteris si es disposen dels models adequats.Reig Bernad, A. (2017). Optimal Control for Automotive Powertrain Applications [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90624TESI

Efficient Automated Driving Strategies Leveraging Anticipation and Optimal Control

Automated vehicles and advanced driver assistance systems bring computation, sensing, and communication technologies that exceed human abilities in some ways. For example, automated vehicles may sense a panorama all at once, do not suffer from human impairments and distractions, and could wirelessly communicate precise data with neighboring vehicles. Prototype and commercial deployments have demonstrated the capability to relieve human operators of some driving tasks up to and including fully autonomous taxi rides in some areas. The ultimate impact of this technology’s large-scale market penetration on energy efficiency remains unclear, with potential negative factors like road use by empty vehicles competing with positive ones like automatic eco-driving. Fundamentally enabled by historic and look-ahead data, this dissertation addresses the use of automated driving and driver assistance to optimize vehicle motion for energy efficiency. Facets of this problem include car following, co-optimized acceleration and lane change planning, and collaborative multi-agent guidance. Optimal control, especially model predictive control, is used extensively to improve energy efficiency while maintaining safe and timely driving via constraints. Techniques including chance constraints and mixed integer programming help overcome uncertainty and non-convexity challenges. Extensions of these techniques to tractor trailers on sloping roads are provided by making use of linear parameter-varying models. To approach the wheel-input energy eco-driving problem over generally shaped sloping roads with the computational potential for closed-loop implementation, a linear programming formulation is constructed. Distributed and collaborative techniques that enable connected and automated vehicles to accommodate their neighbors in traffic are also explored and compared to centralized control. Using simulations and vehicle-in-the-loop car following experiments, the proposed algorithms are benchmarked against others that do not make use of look-ahead information

Control and supervision of an AGV with energy consumption optimization

Os veículos guiados autónomos (AGVs) ganharam enorme importância e interesse no campo da indústria. Estes são soluções populares para o transporte de bens materiais para diferentes partes das fábricas. No entanto, em muitas fábricas, os armazéns estão localizados à parte da linha de produção ou em edifícios separados, exigindo que o transporte de bens materiais seja feito exteriormente. Os ambientes exteriores representam um desafio particular para os AGVs. Por um lado, estes ambientes causam mais desgaste nos componentes dos veículos e o clima na Europa pode atingir extremos opostos, dependendo da estação do ano e das regiões. Por outro lado, estes ambientes aumentam as preocupações de segurança, uma vez que outros veículos ou peões podem circular no mesmo espaço e ao mesmo tempo. Neste projecto, um rebocador eléctrico XXL será transformado num AGV, que opera em ambiente exterior. Este veículo é responsável pelo transporte de mercadorias do final da linha de produção para o armazém exterior numa fábrica de automóveis. O principal objectivo é assegurar o seu funcionamento contínuo durante um turno de 16 horas, garantindo o mínimo de interrupções para v«carregamento da bateria. Desta forma, nesta dissertação foram abordados dois capítulos distintos: para a análise e estudo do consumo energético foi simulado a powertrain de um veículo eléctrico. Neste, foi considerado um motor de indução cujo método de controlo aplicado foi o Field Oriented Control (FOC). Para além do comportamento eléctrico, também foi simulado o modelo físico da carga, bem como o cálculo da energia eléctrica consumida. Para a navegação, foi estudada uma solução baseada na integração do GPS com o INS. Dadas as restrições temporais, apenas a solução GPS foi testada e a técnica Loosely Coupled foi abordada como uma possível solução de integração.Autonomous guided vehicles (AGVs) have gained enormous importance and interest in the industry field. These are popular solutions for transport of good and material to different parts of the factories. However, in many factories, warehouses are located apart from the factory floor or in separate buildings, requiring the transport of material goods to be done outdoors. Outdoor environments represent a particular challenge for AGVs. On one hand, these environments causes more wear and tear on vehicle components and the weather in Europe can reach opposite extremes depending on the season and regions. On the other hand, these environments increase safety concerns since other vehicles or pedestrians can circulate in the same space at the same time. In this project, an electric tugger XXL will be transformed into an AGV, which operates in outdoor environment. This vehicle is responsible for transporting goods from the end of the production line to the outside warehouse in a car manufacturing plant. The main objective is to ensure its continuous operation during a 16-hour shift, and guarantee the minimum battery charging actions. In this way, in this dissertation two distinct chapters were approached: for the analysis and study of the energy consumption it was simulated the powertrain of an electric vehicle. In this one it was considered an induction motor whose control method applied was the Field Oriented Control (FOC). Besides the electrical behaviour, also the physical model of the load was simulated as well as the calculation of the consumed electrical energy. For navigation, a solution based on the integration of GPS with INS was studied. Given the temporal constraints, only the GPS solution was tested and the loosely coupled technique was approached as a possible integration solution

Fuel Consumption Reduction Through Velocity Optimization for Light-Duty Autonomous Vehicles with Different Energy Sources

The emergence of self-driving cars provides an additional flexibility to the vehicle controller, by eliminating the driver and allowing for control of the vehicle's velocity. This work employs constrained optimal control techniques with preview of position constraints, to derive optimal velocity trajectories in a longitudinal vehicle following mode. A framework is developed to compare autonomous driving to human driving, i.e. the Federal Test Procedures of the US Environmental Protection Agency. With just velocity smoothing, improvements by offline global optimization of up to 18% in Fuel Economy (FE), are shown for certain drive cycles in a baseline gasoline vehicle. Applying the same problem structure in an online optimal controller with 1.5 s preview showed a 12% improvement in FE. This work is further extended by using a lead velocity prediction algorithm that provides inaccurate future constraints. For a 10 s prediction horizon, a 10% improvement in FE has been shown. A more conventional procedure for achieving velocity optimization would be the minimization of energy demand at the wheels. This method involves a non-linear model thus increasing optimization complexity and also requires additional information about the vehicle such as mass and drag coefficients. It is shown that even though tractive energy minimization has a lower energy demand than velocity smoothing, smoothing works as well if not better when it comes to reducing fuel consumption. These results are shown to be valid in simulation across three different engines ranging from 1.2 L-turbocharged to 4.3 L-naturally aspirated. The implication of these results is that tractive energy minimization requiring more complex control does not work well for conventional gasoline vehicles. It is further shown that using reduced order powertrain models currently found in literature for velocity optimization, can result in worse FE than previous optimizations. Therefore, an easily implementable, vehicle agnostic velocity smoothing algorithm could be preferred for drive cycle optimization. Employing these same velocity optimization techniques for a battery electric vehicle (BEV) can increase battery range by 15%. It is further demonstrated that eco-driving and regenerative braking are not complimentary and eco-driving is always preferred. Finally, power split optimization has been carried out for a fuel cell hybrid, and it has been shown that a rule-based strategy with drive cycle preview could match the global optimal results.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/149826/1/niketpr_1.pd

Biorefarmeries: Milking ethanol from algae for the mobility of tomorrow

The idea of this project is to fully exploit microalgae to the best of its potential, possibly proposing a sort of fourth generation fuel based on a continuous milking of macro- and microorganisms (as cows in a milk farm), which produce fuel by photosynthetic reactions. This project proposes a new transportation concept supported by a new socio-economic approach, in which biofuel production is based on biorefarmeries delivering fourth generation fuels which also have decarbonization capabilities, potential negative CO2 emissions plus positive impacts on mobility, the automotive Industry, health and environment and the econom

Operational criteria for battlefield vehicles

textModern military ground vehicles are no longer able to respond effectively to the rapidly changing mission requirements of modern military conflicts. Military vehicle architectures, which utilize passive suspension components and traditional drivetrain/steering systems, do not provide the operational flexibility to meet the demands of the operator. Advances in intelligent actuation technology allow for the development of a new vehicle architecture - the Intelligent Corner Vehicle (ICV). The ICV utilizes intelligent actuator technology to actively control the four degrees of freedom of each wheel of the vehicle - drive, camber, steering, and suspension. The utilization of intelligent actuation requires the characterization of the motions and behavior of the tire and the vehicle chassis in order to effectively apply the tire to the road surface - the development of vehicle performance criteria. A brief review of the state of wheeled military systems is presented. Many modern military vehicles were designed to improve protection at the expense of mobility - a process that has had negative effects on vehicle capability. An overview of the pneumatic tire used for wheeled vehicles is presented, highlighting the nonlinearities of tire behavior. The complexity of tire force generation drives the need for the application of intelligent actuation. Traditional actuation of wheel motion is presented along with a variety of current efforts to apply intelligent actuation to individual degrees of freedom of the tire. These efforts can be shown to improve vehicle performance, but intelligent actuation must be applied to all aspects of tire motion, requiring the use of the ICV architecture and the generation of performance criteria by which the complex motion of the vehicle may be evaluated. The Robotics Research Group has a history of developing and evaluating performance criteria for complex dynamic systems. and review of performance criteria developed for serial chain robotics is presented. These criteria address task independent actuator motion in addition to actuator ranges and limits, and their application to the ICV is discussed. A brief overview of several important concepts of classical vehicle dynamics are presented. The application of criteria derived from these concepts to the ICV architecture is discussed. This report presents the complexities of tire behavior and vehicle motion, the need for alternative architectures (the ICV), and a variety of performance criteria required to evaluate vehicle motion in real time. Criteria that are presented are summarized along with their definition and physical meaning. Future work for the development of the ICV involves the generation of a vehicle model for evaluating the application and range values of the presented criteria.Mechanical Engineerin

Who Really Made Your Car?: Restructuring and Geographic Change in the Auto Industry

The authors present the key characteristics of the vast network of auto parts suppliers and describe the changing geography of U.S. motor vehicle production at the local, regional, national, and international levels.https://research.upjohn.org/up_press/1011/thumbnail.jp