Development of a fuel-saving algorithm for a vehicle's driver assistant system

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Mecânica, Florianópolis, 2018.A fim de reduzir o consumo de combustível em sistemas de propulsão automotivos, a implementação de conjuntos motrizes híbridos, o downsizing de motores à combustão interna e a automatização do câmbio têm crescido no mercado de veículos de passeio. No entanto, as melhorias individuais em sistemas de um veículo não necessariamente aproximam a sua operação do ponto de ótima eficiência, e a adição de diferentes fontes de energia deve ser feita de forma metódica e estruturada, a fim de proporcionar ganhos consideráveis em consumo de combustível. Ademais, o comportamento do condutor e as trajetórias percorridas pelo veículo são características extremamente dependentes da região em análise, dificultando ainda mais o desenvolvimento de uma estratégia única de redução de consumo de combustível. Assim, a partir de um modelo de dinâmica longitudinal com três graus de liberdade para um veículo genérico, desenvolvido utilizando as equações de Euler-Lagrange do segundo tipo, essa dissertação tem como objetivo principal a proposta de um algoritmo para um assistente de direção automotivo, o qual promove a redução do consumo de combustível a partir do ajuste da relação de transmissão e abertura da válvula borboleta, em função da demanda de torque imposta pelo condutor, dinâmica do powertrain e características da fonte de potência. As características de desempenho do motor foram modeladas utilizando Redes Neurais Artificiais do tipo Feedforward Multi-Layer Perceptron, viabilizando a simulação de ciclos urbanos em tempo hábil e a inserção de propriedades relacionadas ao gradiente dos mapas estáticos no algoritmo do assistente de direção. O sistema foi implementado e simulado em Matlab , e seu desempenho avaliado através de um estudo de caso, utilizando modelos da literatura como referência.Abstract : The adoption of hybrid powertrain systems in passenger vehicles, as well as downsized engines and automatic transmissions, has been increasing in the last years as solutions to reduce the fuel consumption. However, the individual optimization of components or layout does not necessarily approximates the operation to conditions of maximum efficiency, and the addition of power sources should be done methodically, such that improvements of fuel efficiency can actually be achieved. Furthermore, the behavior of the driver and traffic conditions, factors which have major influence on the fuel consumption, vary with the geographic region, increasing the difficulty to develop a single solution to minimize the fuel consumption. Given such complex scenario, this dissertation proposes an algorithm for a Fuel-saving Driver Assistant System, which actuates on the throttle valve and gearbox, based on the demand of torque imposed by the driver, powertrain dynamics and characteristics of the power sources. In order to do so, a mathematical model of powertrain and longitudinal dynamics with 3 Degrees of Freedom was developed, which allows the simulation of urban traffic conditions. The performance of the engine was modeled using Artificial Neural Networks (ANN), which allies a flexible representation of the nonlinear characteristics of the power source, low computational costs and possibility to derive gradient information from the static maps, which is used by the Driver Assistant Algorithm. The system was implemented on Matlab and its performance compared to different models available in the literature

Testialustan suunnittelu hybridiajoneuvojen hardware-in-the-loop simulaatioihin

Recent changes to vehicle type-approval regulations have increased demand for testing methods, which better represent real-world driving conditions. Hardware-in-the-Loop (HIL) simulation is seen as an attractive alternative for pure simulations and real-world operation measurements. The goal of this work was to provide a functional testbed for engine testing, as well as for HIL simulations of Hybrid Electric Vehicles (HEVs). In addition, a state-of-the-art review of HIL was considered an important goal of the work. The theory behind HIL, and real-time systems in general, is depicted using a wide variety of examples from automotive applications relating to hybrid power sources. The knowledge gained from the literature was used to design and build a testbed in a form of an engine dynamometer. The testbed can be used to emulate rotational forces, such as load torques on a driveshaft. The testbed’s fast hardware connections enable real-time testing. The scope of the design was in mechanical design and in specification of the hardware components. Initial Internal Combustion Engine (ICE) steady-state and transient tests were done to partially validate the testbed. However, the performance was assessed to not be at an acceptable level. For example, only speed tracking passed the non-road transient cycle tracking assessment. Torque tracking and the derived power curves failed the assessment narrowly. However, the test results indicate that with proper tuning of the control software, the system performance should get better. The system response was slow at this point, but the transient behavior itself was fast. Also, in steady-state, torque and speed ripple were low. Only the preparations for HIL simulation were carried out, since the testbed was not validated to be functional enough for the much more demanding HIL tests. The preparations involved building a simulation model of a series-parallel hybrid Refuse-Collecting Vehicle (RCV), which is to be used for the verification of the designed system’s HIL capabilities. The model was independently verified to be suitable to be used for the physical tests.Viimeaikaiset muutokset ajoneuvojen tyyppihyväksyntään ovat lisänneet tarvetta testausmetodeille, jotka paremmin vastaavat oikean elämän ajo-olosuhteita. HIL-simulaatio nähdään houkuttelevana vaihtoehtona pelkälle simulaatiolle sekä ajoneuvon ajonaikaisille mittauksille. Tämän työn tavoitteena on tarjota toimiva testilaite moottoridynamometritestaukseen sekä hybridiajoneuvojen HIL-simulaatioihin. Lisäksi, HIL:in nykytilanteen kuvausta pidettiin tärkeänä työn tavoitteena. HIL:in, ja yleisemmin reaaliaikaisen testauksen, tausta ja teoria selvitettiin laaja alaisesti käyttäen esimerkkejä hybridivoimanlähteisiin liittyvistä ajoneuvoalan käyttökohteista. Kirjallisuutta hyödyntäen, testipenkki suunniteltiin ja rakennettiin. Testipenkkiä voidaan käyttää emuloimaan pyöriviä voimia, kuten vetoakseliin kohdistuvia vääntöjä. Testipenkin nopeat yhteydet mahdollistavat reaaliaikaisen testauksen. Suunnittelu oli rajattu pääasiassa mekaaniseen suunnitteluun ja komponenttien määrittelyyn. Sähkö- ja ohjelmistosuunnittelu määriteltiin yleisellä tasolla. Alustavat polttomoottorilla tehdyt vakaiden ajopisteiden ja transienttiajojen testit toteutettiin testipenkin osittaiseksi validoinniksi. Kuitenkin, laitteen suorituskyky ei yltänyt halutulle tasolle. Esimerkiksi, ainoastaan nopeusseuranta läpäisi transienttiajo testin, mutta vääntö- ja voimaseurannat epäonnistuivat täpärästi. Tulokset kuitenkin osoittavat luottamusta siitä että testipenkki saadaan aikanaan halutulle tasolle ohjelmistopuolen kontrollereja säätämällä. Tällä hetkellä systeemin vasteaika on liian pitkä, vaikka muuten dynamiikka on nopeaa. Lisäksi, vakaissa ajopisteissä vääntö- ja nopeushuojunta ovat alhaisia. Ainoastaan valmistelut HIL-simulaatiota varten saatiin toteutettua, sillä testipenkkiä ei saatu reaaliaikasta testausta vaativalle tasolle. Valmistelut sisälsivät hybridijäteauton simulaatiomallin rakentamisen, jota tullaan aikanaan käyttämään testipenkin HIL toimivuuden validointiin. Simulaatiomalli varmistettiin itsenäisenä toimivaksi, ja siten soveltuvaksi tuleviin fyysisiin testiajoihin

A New Powertrain Architecture: From Electromagnetic-Structural Dynamics to Platooning

Electrification and vehicle-to-vehicle connectivity have become two of the major areas of vehicle development in recent years. Electrified vehicles show significant advantages because of their high performance in fuel economy and low emissions compared to conventional vehicles. Although hybrid electric vehicle (HEV) development has resulted in a variety of powertrain architectures, novel high-performance powertrain solutions with fewer components and low cost remain an important need. In addition, common HEV configurations use small internal combustion engines, which can suffer from high torque fluctuations detrimental for NVH performance and safety. Advanced powertrains that absorb these fluctuations efficiently are needed. This thesis presents a novel HEV powertrain architecture without any planetary gears or clutches. Using physics-based component model, a proof-of-concept powertrain model is implemented and demonstrated ability to remove over 99.5% torque fluctuation and fulfill vehicle driving demands. A comprehensive design and control optimization for the novel powertrain is performed. A single utility function is designed by combining multiple objectives, and is tuned using the Pareto front of the novel powertrain performance to obtain different optimal powertrain designs. Optimal novel powertrain designs show comparable performance with optimal designs of commercially available power-split benchmark powertrains. Torque fluctuations in HEVs may result in electromagnetic-structural (EMS) phenomena within the electric machines of the powertrain. Periodic forces generated by permanent magnets or windings and other disturbances to the EM device can lead to excitation of specific structural resonances due to EMS coupling. Existing EMS models are usually 2D and do not capture the EMS coupling. Thus, a model that accurately and efficiently captures EMS phenomena is required. To capture the EMS phenomena, displacement-dependent EM forces are introduced in the modal space to the structural dynamics of electric machines. Both linear and nonlinear approximations of EM forces are calculated using high-fidelity FEA models, forming a reduced-order model (ROM) with EMS coupling, namely the EMS ROM. The dynamics of the EMS ROM is similar to a damped dynamical system governed by Mathieu's equation, which exhibits parametric excitation. The EMS ROM is used to compute the stability transition threshold for the parametric excitation. Parametric resonance peaks are revealed in the responses from an unstable device with EMS. In addition, a frequency shift of the primary resonance peak caused by (nonlinear) EM force harmonics is detected. Time-domain analyses using the high-fidelity FEA model confirm the EMS phenomena and accuracy of the EMS ROM. Multiple vehicles, each with an advanced powertrain can be used in platoons to enhance fuel economy, road capacity, and safety compared to a single vehicle. Studies that focus on platooning usually do not focus on task-based longitudinal planning and do not capture detailed powertrain operations, which impact the control and energy consumption of the overall platoon. In this thesis, multiple vehicles, each equipped with the novel powertrain, are investigated when they form a platoon and drive on a specified path. The drive schedule and vehicle controllers are optimized to minimize the total energy consumption of the platoon. Energy optimization requires an integrated vehicle-following model and a high-fidelity powertrain model. In addition, component-level, vehicle-level, and platoon-level constraints are applied. Parametric studies are performed for both homogeneous and heterogeneous platoons. Optimization is shown to effectively reduce the maximum headway error by an order of magnitude and enhance energy saving of 17% to 37%.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/166142/1/albertyi_1.pd

Echtzeit-Strategieplanung für vorausschauendes automatisiertes Fahren

Im Rahmen der vorliegenden Arbeit wird ein Fahrerassistenzsystem für vorausschauendes automatisiertes Fahren entwickelt. Es umfasst die Längs- und Querführung des Fahrzeugs sowie die Steuerung der relevanten Triebstrangkomponenten. Dabei werden Vorausschauinformationen über die Fahrzeugumgebung ausgewertet, um ein energie- und komfortoptimales Fahrverhalten zu erreichen. Für die echtzeitfähige optimale Regelung wird ein Stabilisierungsansatz hergeleitet, der die Regelungsaufgabe auf eine Strategie- und eine Stabilisierungsebene verteilt. Er verbindet die für eine genaue Strategieplanung notwendige lange Zykluszeit mit einer hochfrequenten, optimalen Störungskompensation. Zur Planung auf Strategie- und Stabilisierungsebene wird ein dreistufiges Verfahren entworfen. Es setzt sich aus einer regelbasierten Einschränkung des Suchraums, einer Initialschätzung mittels Dynamischer Programmierung und einer lokalen Suche nach der Optimaltrajektorie zusammen; die Suche wird zusätzlich durch Heuristiken und bestehendes Vorwissen gesteuert. Es wird eine Methodik hergeleitet, um das System hinsichtlich Regelgüte und Berechnungsaufwand optimal auszulegen. Die Einflüsse von Stabilisierungsansatz sowie Horizont und Genauigkeit der Trajektorienplanung werden dafür simulativ ausgewertet. Zur Simulation der vorausschauenden Regelung wird ein Ansatz entwickelt, der es ermöglicht, in Versuchsfahrten gemessene Fahrzeug- und Umgebungsdaten mit einem reaktiven Fahrzeugmodell zu kombinieren. Die Funktionsweise des Assistenzsystems im realen Fahrbetrieb wird am Beispiel verschiedener Fahrsituationen exemplarisch diskutiert

Echtzeit-Strategieplanung für vorausschauendes automatisiertes Fahren

Im Rahmen der Arbeit wird ein Fahrerassistenzsystem für vorausschauendes automatisiertes Fahren entwickelt. Es umfasst die Längs- und Querführung des Fahrzeugs sowie die Steuerung der relevanten Triebstrangkomponenten. Dabei werden Vorausschauinformationen über die Fahrzeugumgebung ausgewertet, um ein energie- und komfortoptimales Fahrverhalten zu erreichen