Estudio del sector automotriz para el ingreso de automóviles híbridos en Bogotá-Colombia

Se hizo un estudio exploratorio de carácter prospectivo, para identificar las opciones y alternativas que pueden encontrarse en el mercado del sector automotor para el ingreso de una gama de vehículos híbridos o ecológicos, de tal manera que se puedan entregar elementos de juicio a los interesados en invertir en este ramo, que les permitan tomar decisiones estratégicas en este sentido. El estudio contemplo los aspectos de mercadeo, estudio estratégico sectorial y un estudio de escenarios de futuro. El estudio de escenarios de futuro se llevo a cabo mediante la metodología prospectiva de Michael Godet, en la cual, a partir del abordaje del problema como un sistema, se identifican preliminarmente las variables que lo integran y las relaciones que existen entre ellas desde los conceptos de influencia y dependencia que pueden generar variaciones del mismo en el tiempo. Dentro de la misma metodología se hace además una identificación de los actores más importantes del sistema, y a partir de las consultas de expertos y bibliografía, se establecen sus comportamientos característicos y la forma como estos pueden influenciar a las variables claves y por lo tanto a la estabilidad del sistema en el tiempo. A partir de la información anterior, es decir una vez identificadas las variables claves, se definen los objetivos estratégicos, y se exponen los posibles escenarios de futuro de acuerdo con las graficas de probabilidades que arroja el sistema, de estos escenarios de futuro se identifica el escenario ideal o el escenario apuesta.

Simulazione dinamica di un veicolo dotato di powertrain ibrido endotermico-elettrico

Nel panorama motoristico ed automobilistico moderno lo sviluppo di motori a combustione interna e veicoli è fortemente influenzato da diverse esigenze che spesso sono in contrasto le une con le altre. Infatti gli obiettivi di economicità e riduzione dei costi riguardanti la produzione e la commercializzazione dei prodotti sono in contrasto con gli sforzi che devono essere operati dalle case produttrici per soddisfare le sempre più stringenti normative riguardanti le emissioni inquinanti ed i consumi di carburante dei veicoli. Fra le numerose soluzioni presenti i veicoli ibridi rappresentano una alternativa che allo stato attuale è già presente sul mercato in varie forme, a seconda della tipologie di energie accoppiate. In letteratura è possibile trovare numerosi studi che trattano l’ottimizzazione dei componenti o delle strategie di controllo di queste tipologie di veicoli: in moltissimi casi l’obiettivo è quello di minimizzare consumi ed emissioni inquinanti. Normalmente non viene posta particolare attenzione agli effetti che l’aggiunta delle macchine elettriche e dei componenti necessari per il funzionamento delle stesse hanno sulla dinamica del veicolo. Il presente lavoro di tesi è incentrato su questi aspetti: si è considerata la tipologia di veicoli ibridi termici-elettrici di tipo parallelo andando ad analizzare come cambiasse il comportamento dinamico del veicolo in funzione del tipo di installazione considerato per la parte elettrica del powertrain. In primo luogo è stato quindi necessario costruire ed implementare un modello dinamico di veicolo che permettesse di applicare coppie alle quattro ruote in maniera indipendente per considerare diverse tipologie di powertrain. In seguito si sono analizzate le differenze di comportamento dinamico fra il veicolo considerato e l’equivalente versione ibrida e i possibili utilizzi delle macchine elettriche per correggere eventuali deterioramenti o cambiamenti indesiderati nelle prestazioni del veicolo.

Technological trajectories and environmental policy: the transformation of the automobile

This thesis aims to fill the gap in the literature by examining the relationship between technological trajectories and environmental policy in the automotive industry, focusing on the role of environmental policies in unlocking the industry from fossil fuel path-dependence. It first explores the inducement mechanism that underpins the interaction between environmental policy and green technological advances, investigating under what conditions the European environmental transport policy portfolio and the intrinsic characteristics of assignees' knowledge boost worldwide green patent production. Subsequently, the thesis empirically analyses the dynamics of technological knowledge involved in technological trajectories assessing evolution patterns such as variation, selection and retention, in order to study the impact of policy implementation on technological knowledge related to electric and hybrid vehicle technologies. Finally, the thesis sheds light on the drivers that encourage a shift from incumbent internal combustion engine technologies towards low-emission vehicle technologies. This analysis tests whether tax-inclusive fuel prices and technological proximity between technological fields induce a shift from non-environmental inventions to environmentally friendly inventive activities and if they impact the competition between alternative vehicle technologies. The findings provide insights into the effectiveness of environmental policy in triggering inventive activities related to the development of alternative vehicle technologies. In addition, there is evidence that environmental policy redirects technological efforts towards a sustainable path and impacts the competition between low-emission vehicles.

DEVELOPMENT OF THE ECOCAR 3 PROPOSAL AND GUIDELINES FOR MODELING AND DESIGN IN YEAR ONE OF ECOCAR 3

This report summarizes the work done for the Vehicle Powertrain Modeling and Design Problem Proposal portion of the EcoCAR3 proposal as specified in the Request for Proposal from Argonne National Laboratory. The results of the modeling exercises presented in the proposal showed that: An average conventional vehicle powered by a combustion engine could not meet the energy consumption target when the engine was sized to meet the acceleration target, due the relatively low thermal efficiency of the spark ignition engine. A battery electric vehicle could not meet the required range target of 320 km while keeping the vehicle weight below the gross vehicle weight rating of 2000 kg. This was due to the low energy density of the batteries which necessitated a large, and heavy, battery pack to provide enough energy to meet the range target. A series hybrid electric vehicle has the potential to meet the acceleration and energy consumption parameters when the components are optimally sized. A parallel hybrid electric vehicle has less energy conversion losses than a series hybrid electric vehicle which results in greater overall efficiency, lower energy consumption, and less emissions. For EcoCAR3, Michigan Tech proposes to develop a plug-in parallel hybrid vehicle (PPHEV) powered by a small Diesel engine operating on B20 Bio-Diesel fuel. This architecture was chosen over other options due to its compact design, lower cost, and its ability to provide performance levels and energy efficiency that meet or exceed the design targets. While this powertrain configuration requires a more complex control system and strategy than others, the student engineering team at Michigan Tech has significant recent experience with this architecture and has confidence that it will perform well in the events planned for the EcoCAR3 competition.

Optimización del consumo de combustible de un vehículo dependiendo de las condiciones de la carretera

El consumo de combustible en un automóvil es una característica que se intenta mejorar continuamente debido a los precios del carburante y a la creciente conciencia medioambiental. Esta tesis doctoral plantea un algoritmo de optimización del consumo que tiene en cuenta las especificaciones técnicas del vehículo, el perfil de orografía de la carretera y el tráfico presente en ella. El algoritmo de optimización calcula el perfil de velocidad óptima que debe seguir el vehículo para completar un recorrido empleando un tiempo de viaje especificado. El cálculo del perfil de velocidad óptima considera los valores de pendiente de la carretera así como también las condiciones de tráfico vehicular de la franja horaria en que se realiza el recorrido. El algoritmo de optimización reacciona ante condiciones de tráfico cambiantes y adapta continuamente el perfil óptimo de velocidad para que el vehículo llegue al destino cumpliendo el horario de llegada establecido. La optimización de consumo es aplicada en vehículos convencionales de motor de combustión interna y en vehículos híbridos tipo serie. Los datos de consumo utilizados por el algoritmo de optimización se obtienen mediante la simulación de modelos cuasi-estáticos de los vehículos. La técnica de minimización empleada por el algoritmo es la Programación Dinámica. El algoritmo divide la optimización del consumo en dos partes claramente diferenciadas y aplica la Programación Dinámica sobre cada una de ellas. La primera parte corresponde a la optimización del consumo del vehículo en función de las condiciones de tráfico. Esta optimización calcula un perfil de velocidad promedio que evita, cuando es posible, las retenciones de tráfico. El tiempo de viaje perdido durante una retención de tráfico debe recuperarse a través de un aumento posterior de la velocidad promedio que incrementaría el consumo del vehículo. La segunda parte de la optimización es la encargada del cálculo de la velocidad óptima en función de la orografía y del tiempo de viaje disponible. Dado que el consumo de combustible del vehículo se incrementa cuando disminuye el tiempo disponible para finalizar un recorrido, esta optimización utiliza factores de ponderación para modular la influencia que tiene cada una de estas dos variables en el proceso de minimización. Aunque los factores de ponderación y la orografía de la carretera condicionan el nivel de ahorro de la optimización, los perfiles de velocidad óptima calculados logran ahorros de consumo respecto de un perfil de velocidad constante que obtiene el mismo tiempo de recorrido. Las simulaciones indican que el ahorro de combustible del vehículo convencional puede lograr hasta un 8.9% mientras que el ahorro de energía eléctrica del vehículo híbrido serie un 2.8%. El algoritmo fusiona la optimización en función de las condiciones del tráfico y la optimización en función de la orografía durante el cálculo en tiempo real del perfil óptimo de velocidad. La optimización conjunta se logra cuando el perfil de velocidad promedio resultante de la optimización en función de las condiciones de tráfico define los valores de los factores de ponderación de la optimización en función de la orografía. Aunque el nivel de ahorro de la optimización conjunta depende de las condiciones de tráfico, de la orografía, del tiempo de recorrido y de las características propias del vehículo, las simulaciones indican ahorros de consumo superiores al 6% en ambas clases de vehículo respecto a optimizaciones que no logran evitar retenciones de tráfico en la carretera. ABSTRACT Fuel consumption of cars is a feature that is continuously being improved due to the fuel price and an increasing environmental awareness. This doctoral dissertation describes an optimization algorithm to decrease the fuel consumption taking into account the technical specifications of the vehicle, the terrain profile of the road and the traffic conditions of the trip. The algorithm calculates the optimal speed profile that completes a trip having a specified travel time. This calculation considers the road slope and the expected traffic conditions during the trip. The optimization algorithm is also able to react to changing traffic conditions and tunes the optimal speed profile to reach the destination within the specified arrival time. The optimization is applied on a conventional vehicle and also on a Series Hybrid Electric vehicle (SHEV). The fuel consumption optimization algorithm uses data obtained from quasi-static simulations. The algorithm is based on Dynamic Programming and divides the fuel consumption optimization problem into two parts. The first part of the optimization process reduces the fuel consumption according to foreseeable traffic conditions. It calculates an average speed profile that tries to avoid, if possible, the traffic jams on the road. Traffic jams that delay drivers result in higher vehicle speed to make up for lost time. A higher speed of the vehicle within an already defined time scheme increases fuel consumption. The second part of the optimization process is in charge of calculating the optimal speed profile according to the road slope and the remaining travel time. The optimization tunes the fuel consumption and travel time relevancies by using two penalty factors. Although the optimization results depend on the road slope and the travel time, the optimal speed profile produces improvements of 8.9% on the fuel consumption of the conventional car and of 2.8% on the spent energy of the hybrid vehicle when compared with a constant speed profile. The two parts of the optimization process are combined during the Real-Time execution of the algorithm. The average speed profile calculated by the optimization according to the traffic conditions provides values for the two penalty factors utilized by the second part of the optimization process. Although the savings depend on the road slope, traffic conditions, vehicle features, and the remaining travel time, simulations show that this joint optimization process can improve the energy consumption of the two vehicles types by more than 6%.

Dysprosium transport in Nd-Fe-B pellets

The addition of heavy rare earth (RE) elements to Nd2Fe14B based magnets to form (Nd,Dy)2Fe14B is known to increase the coercivity and high temperature performance required for hybrid vehicle electric motors and other extreme temperature applications. Attempts to conserve heavy rare earth elements for high temperature (RE)2Fe14B based magnets have led to the development of a grain boundary diffusion process for bulk magnets. This process relies on transport of a heavy rare earth, such as Dy, into a bulk Nd2Fe14B magnet along pores, a low volume fraction of eutectic liquid along grain boundary grain triple junctions and grain boundaries. This enriches the grain surfaces in Dy through the thickness of the bulk magnet, leading to larger increases coercivity with a smaller Dy concentration than can be achieved with homogeneous alloys. Attempts to carry out the same process during sintering require significant control of Dy transport efficiency. The macroscopic transport of Dy in Nd2.7Fe14B1.4 based powder packs is studied using a 'layered' pellet, where Nd2.7Fe14B1.4powder is an interlayer and Dy source as a center layer. The sintering of this layered pellet provided evidence for very large effective diffusion lengths aided by Dy rich liquid flow through connected porosity. Approaches to controlling Dy transportation include decreasing the liquid phase transport capability of the powder pack by increasing the melting point of the Dy source and the decreasing amount of RE rich liquid in the powder packs. The solid-liquid reaction is studied in which melt spun Nd2.7Fe14B1.4 ribbons are PVD coated with Dy-Fe eutectic composition and then thermally treated. The resulting microstructure from the reaction between Dy-Fe eutectic coating and Nd2.7Fe14B1.4 ribbon is interpreted as support for a proposed dissolution/reprecipitation process between solid and liquid phases. The estimate the diffusion coefficient and the effective diffusion length of Dy sources in Nd2.7Fe14B1.4 layered pellets and melt spun ribbons were obtained from the calculation of Fick's second law combined with EDS results from the experiment. The results indicate that the effective diffusion coefficient of Dy in the layered pellets is higher than the diffusion in ribbons due to its higher porosity than ribbons.

Decision support tool for Virtual Power Players: Hybrid Particle Swarm Optimization applied to Day-ahead Vehicle-To-Grid Scheduling

This paper presents a decision support tool methodology to help virtual power players (VPPs) in the Smart Grid (SGs) context to solve the day-ahead energy resource scheduling considering the intensive use of Distributed Generation (DG) and Vehicle-To-Grid (V2G). The main focus is the application of a new hybrid method combing a particle swarm approach and a deterministic technique based on mixedinteger linear programming (MILP) to solve the day-ahead scheduling minimizing total operation costs from the aggregator point of view. A realistic mathematical formulation, considering the electric network constraints and V2G charging and discharging efficiencies is presented. Full AC power flow calculation is included in the hybrid method to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Design and Control of Permanent Magnet Synchronous Machine for Hybrid Electric Vehicle

This master’s thesis mainly focuses on the design requirements of an Electric drive for Hybrid car application and its control strategy to achieve a wide speed range. It also emphasises how the control and performance requirements are transformed into its design variables. A parallel hybrid topology is considered where an IC engine and an electric drive share a common crank shaft. A permanent magnet synchronous machine (PMSM) is used as an electric drive machine. Performance requirements are converted into Machine design variables using the vector model of PMSM. Main dimensions of the machine are arrived using analytical approach and Finite Element Analysis (FEA) is used to verify the design and performance. Vector control algorithm was used to control the machine. The control algorithm was tested in a low power PMSM using an embedded controller. A prototype of 10 kW PMSM was built according to the design values. The prototype was tested in the laboratory using a high power converter. Tests were carried out to verify different operating modes. The results were in agreement with the calculations.

Minimizing residential distribution system operating costs through intelligently scheduled plug-in hybrid electric vehicle charging

Rising fuel prices and environmental concerns are threatening the stability of current electrical grid systems. These factors are pushing the automobile industry towards more effcient, hybrid vehicles. Current trends show petroleum is being edged out in favor of electricity as the main vehicular motive force. The proposed methods create an optimized charging control schedule for all participating Plug-in Hybrid Electric Vehicles in a distribution grid. The optimization will minimize daily operating costs, reduce system losses, and improve power quality. This requires participation from Vehicle-to-Grid capable vehicles, load forecasting, and Locational Marginal Pricing market predictions. Vehicles equipped with bidirectional chargers further improve the optimization results by lowering peak demand and improving power quality.

Development of Hardware-In-the-Loop Simulation System for Hybrid Electric Vehicle Study

The development of embedded control systems for a Hybrid Electric Vehicle (HEV) is a challenging task due to the multidisciplinary nature of HEV powertrain and its complex structures. Hardware-In-the-Loop (HIL) simulation provides an open and convenient environment for the modeling, prototyping, testing and analyzing HEV control systems. This thesis focuses on the development of such a HIL system for the hybrid electric vehicle study. The hardware architecture of the HIL system, including dSPACE eDrive HIL simulator, MicroAutoBox II and MotoTron Engine Control Module (ECM), is introduced. Software used in the system includes dSPACE Real-Time Interface (RTI) blockset, Automotive Simulation Models (ASM), Matlab/Simulink/Stateflow, Real-time Workshop, ControlDesk Next Generation, ModelDesk and MotoHawk/MotoTune. A case study of the development of control systems for a single shaft parallel hybrid electric vehicle is presented to summarize the functionality of this HIL system.

STUDY OF HYBRID ELECTRIC VEHICLE BATTERY MODELING AND CONTROL USING AUTONOMIE

This report presents the research results of battery modeling and control for hybrid electric vehicles (HEV). The simulation study is conducted using plug-and-play powertrain and vehicle development software, Autonomie. The base vehicle model used for testing the performance of battery model and battery control strategy is the Prius MY04, a power-split hybrid electric vehicle model in Autonomie. To evaluate the battery performance for HEV applications, the Prius MY04 model and its powertrain energy flow in various vehicle operating modes are analyzed. The power outputs of the major powertrain components under different driving cycles are discussed with a focus on battery performance. The simulation results show that the vehicle fuel economy calculated by the Autonomie Prius MY04 model does not match very well with the official data provided by the department of energy (DOE). It is also found that the original battery model does not consider the impact of environmental temperature on battery cell capacities. To improve battery model, this study includes battery current loss on coulomb coefficient and the impact of environmental temperature on battery cell capacity in the model. In addition, voltage losses on both double layer effect and diffusion effect are included in the new battery model. The simulation results with new battery model show the reduced fuel economy error to the DOE data comparing with the original Autonomie Prius MY04 model.

Dual Active Bridge based Battery Charger for Plug-in Hybrid Electric Vehicle with Charging Current Containing Low Frequency Ripple

High power density is strongly preferable for the on-board battery charger of Plug-in Hybrid Electric Vehicle (PHEV). Wide band gap devices, such as Gallium Nitride HEMTs are being explored to push to higher switching frequency and reduce passive component size. In this case, the bulk DC link capacitor of AC-DC Power Factor Correction (PFC) stage, which is usually necessary to store ripple power of two times the line frequency in a DC current charging system, becomes a major barrier on power density. If low frequency ripple is allowed in the battery, the DC link capacitance can be significantly reduced. This paper focuses on the operation of a battery charging system, which is comprised of one Full Bridge (FB) AC-DC stage and one Dual Active Bridge (DAB) DC-DC stage, with charging current containing low frequency ripple at two times line frequency, designated as sinusoidal charging. DAB operation under sinusoidal charging is investigated. Two types of control schemes are proposed and implemented in an experimental prototype. It is proved that closed loop current control is the better. Full system test including both FB AC-DC stage and DAB DC-DC stage verified the concept of sinusoidal charging, which may lead to potentially very high power density battery charger for PHEV.

Applicability of Federal motor vehicle standards to electric and hybrid vehicles.

"DOT HS 802 611."

Performance testing and system characterization. DOE Hybrid Test Vehicle phase III: final report.

Energy Department, Washington, D.C.

Improved hybrid computer vehicle handling program. Final report.

National Highway Traffic Safety Administration, Washington, D.C.