Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Abstract-A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems.This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubspermissions@ieee.orgThe authors wish to acknowledge financial support from the Generalitat de Catalunya (GRC MCIA, Grant n SGR 2014-101).J. Lopez-Sanz and G. sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2• C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Index Terms-nonlinear model predictive control (NMPC), thermal management, plug-in hybrid electric vehicles (PHEV), Li-ion battery cooling.
Abstract-A nonlinear model predictive control (NMPC) for the thermal management (TM) of Plug-in Hybrid Electric Vehicles (PHEVs) is presented. TM in PHEVs is crucial to ensure good components performance and durability in all possible climate scenarios. A drawback of accurate TM solutions is the higher electrical consumption due to the increasing number of low voltage (LV) actuators used in the cooling circuits. Hence, more complex control strategies are needed for minimizing components thermal stress and at the same time electrical consumption. In this context, NMPC arises as a powerful method for achieving multiple objectives in Multiple input-Multiple output systems.This paper proposes an NMPC for the TM of the High Voltage (HV) battery and the power electronics (PE) cooling circuit in a PHEV. It distinguishes itself from the previously NMPC reported methods in the automotive Copyright (c) 2015 IEEE. Personal use of this material is permitted. However, permission to use this material for any other purposes must be obtained from the IEEE by sending a request to pubspermissions@ieee.orgThe authors wish to acknowledge financial support from the Generalitat de Catalunya (GRC MCIA, Grant n SGR 2014-101).J. Lopez-Sanz and G. sector by the complexity of its controlled plant which is highly nonlinear and controlled by numerous variables. The implemented model of the plant, which is based on experimental data and multi-domain physical equations, has been validated using six different driving cycles logged in a real vehicle, obtaining a maximum error, in comparison with the real temperatures, of 2• C. For one of the six cycles, an NMPC software-in-the loop (SIL) is presented, where the models inside the controller and for the controlled plant are the same. This simulation is compared to the finite-state machine-based strategy performed in the real vehicle. The results show that NMPC keeps the battery at healthier temperatures and in addition reduces the cooling electrical consumption by more than 5%. In terms of the objective function, an accumulated and weighted sum of the two goals, this improvement amounts 30%. Finally, the online SIL presented in this paper, suggests that the used optimizer is fast enough for a future implementation in the vehicle.Index Terms-nonlinear model predictive control (NMPC), thermal management, plug-in hybrid electric vehicles (PHEV), Li-ion battery cooling.
Several socioeconomic factors are leading governments to encourage electric powered vehicles. Currently, the bottleneck for electric vehicles mass production lies in the high voltage battery technology. One of the main challenges to ensure batteries safety, comfort, performance and durability requirements is thermal management, since operating at temperatures outside the range specified by the manufacturer, they age prematurely, lead to dangerous and uncontrolled exothermic reactions and/or be incapable of delivering the electric energy demand to move the vehicle. The tendency in the solutions design for thermal management is to use cooling circuits with more and more sophisticated architectures governed by an increasing number of electrical actuators like pumps, fans and solenoid valves. The control of these systems is complex due to their nonlinear behavior, the high number of inputs and outputs and the need of accomplishing multiple goals, usually contradictory, at the same time. In front of this class of problems, conventional control methods are taken to their limit and new optimization based methods, like model predictive control, capable of exploiting the full potential in this kind of systems, are attracting the attention of the sector. The present thesis deals with the design of a predictive control for the battery and power electronics cooling circuit in a Plug-In Hybrid Electric Vehicle. The main merit of the proposed solution is that the method validation takes places in a prototype on real-time, which, as it will be seen in the state of the art, is one of the usual lacks in most model predictive control publications in the automotive sector. For reaching this settlement, the development of a suitable model of the system and optimization problem definition together with the use of an efficient and robust numerical tool, have been essential and therefore will be addressed exhaustively in this document. Additionally, the validation by means of simulation as well as the design of repeatable driving conditions for comparing the proposed control with the original one in the vehicle will be shown before reaching the final validation and discussion. Diversos factores socioeconómicos están llevando a los gobiernos a fomentar los vehículos propulsados eléctricamente. Actualmente, el cuello de botella para la producción en masa del vehículo eléctrico reside en la tecnología de la batería de alta tensión. Uno de los retos principales para asegurar las prestaciones de seguridad, confort, funcionamiento y durabilidad de la batería es la gestión térmica, ya que a temperaturas alejadas de las especificadas por el fabricante, ésta envejece de forma prematura, dar lugar a una peligrosa y descontrolada reacción exotérmica y/o ser incapaz de entregar la energía eléctrica necesaria para mover el vehículo. La tendencia en el diseño de soluciones para la gestión térmica, es la de usar circuitos de refrigeración con arquitecturas cada vez más sofisticadas que implican la necesidad de un mayor número de actuadores eléctricos como bombas, ventiladores y electroválvulas. El control de estos sistemas es complejo debido a su comportamiento no lineal, al elevado número de entradas y salidas y a la necesidad de lograr varios objetivos a la vez a menudo contradictorios. Ante esta clase de problemas, los métodos convencionales de control son llevados a su límite y nuevos métodos basados en optimización, como el control predictivo, capaces de explotar el potencial de este tipo de sistemas, empiezan a atraer la atención del sector. Esta tesis trata del diseño de un control predictivo para la gestión térmica del circuito de refrigeración de la batería y la electrónica de potencia en un vehículo híbrido enchufable. El principal mérito de la solución propuesta es la validación del método en un prototipo en tiempo real, que según se verá en la revisión del estado del arte, es una de las principales carencias en la mayoría de estudios de esta técnica en automoción. Para llegar a esta solución, el desarrollo de un modelo del sistema adecuado y la definición del problema de optimización en combinación con el uso de una herramienta numérica fiable y robusta, han sido imprescindibles y por eso ocuparán una parte importante de este documento. Asimismo la validación por medio de simulación previa a la experimental, así como el diseño de unas condiciones de conducción repetibles, para comparar el control propuesto con el original del vehículo, serán tratadas antes de llegar a la validación y discusión finales Diversos factors socioeconòmics estan portant als governs a fomentar els vehicles propulsats elèctricament. Actualment, el coll d’ampolla per a la producció en massa del vehicle elèctric resideix a la tecnologia de la bateria d’alta tensió. Un dels reptes principals per assegurar les prestacions de seguretat, confort, funcionament i durabilitat de la bateria és la gestió tèrmica, ja que a temperatures allunyades de les especificades pel fabricant, aquesta envelleix de forma prematura, donar lloc a una perillosa i descontrolada reacció exotèrmica i/o ser incapaç de lliurar l’energia elèctrica necessària per moure el vehicle. La tendència en el disseny de solucions per a la gestió tèrmica, és la d’usar circuits de refrigeració amb arquitectures cada vegada més sofisticades que impliquen la necessitat d’un major nombre d’actuadors elèctrics com a bombes, ventiladors i electrovàlvules. El control d’aquests sistemes és complex a causa del seu comportament no lineal, a l’elevat nombre d’entrades i sortides i a la necessitat de assolir diversos objectius alhora sovint contradictoris. Davant aquesta classe de problemes, els mètodes convencionals de control són portats al seu límit i nous mètodes basats en optimització, com el control predictiu, capaços d’explotar el potencial d’aquest tipus de sistemes, comencen a atreure l’atenció del sector. Aquesta tesi tracta del disseny d’un control predictiu per a la gestió tèrmica del circuit de refrigeració de la bateria i l’electrònica de potència en un vehicle híbrid endollable. El principal mèrit de la solució proposada és la validació del mètode en un prototip en temps real, que segons es veurà en la revisió de l’estat de l’art, és una de les principals manques en la majoria d’estudis d’aquesta tècnica en automoció. Per arribar a aquesta solució, el desenvolupament d’un model del sistema adequat i la definició del problema d’optimització en combinació amb l’ús d’una eina numèrica fiable i robusta, han estat imprescindibles i per això ocuparan una part important d’aquest document. Així mateix la validació per mitjà de simulació prèvia a l’experimental, així com el disseny d’unes condicions de conducció repetibles, per comparar el control proposat amb l’original del vehicle, seran tractades abans d’arribar a la validació i discussió finals. Elektrisch angetriebene Fahrzeuge werden derzeit aus unterschiedlichen sozioökonomischen Gründen von den Regierungen gefördert. Der Flaschenhals für die Massenproduktion von dieser Technologie ist die Hochvoltbatterie, die mehrere Herausforderungen um die Sicherheit, Komfort, Performance und Lebensdauer Anforderungen zu erfüllen begegnet. Das Thermomanagement ist eine davon, da der Batterie Temperaturbetrieb außerhalb des vom Hersteller angegebenen Bereiches führ zur vorzeitigen Alterung, gefährlichen und unkontrollierten exothermischen Reaktionen und Stromversorgungsbeschränkungen, die den Antrieb des Fahrzeugs verhindern. Der Trend in das Design von Thermomanagementlösungen geht dahin, immer ausgeklügelter Kühlkreislaufarchitekturen zu verwenden, wobei eine steigende Anzahl von elektrischen Aktoren wie Pumpen, Lüftern und Ventilen, benötigt werden. Die Regelung solcher Systeme ist komplex aufgrund ihres nichtlinearen Verhaltens, der zahlreichen Eingängen und Ausgängen und des Bedürfnisses mehrere Ziele gleichzeitig zu erfüllen. Angesichts dieser Aufgabenstellung, werden die konventionelle Regelungsmethoden an ihre Grenzen gebracht und neue optimierungsbasierten Methoden wie die modellbasierte prädiktive Regelung, die das volle Potential solcher Systeme ausnutzen können, beginnen die Aufmerksamkeit der Autoindustrie auf sich zu lenken. Die vorliegende Arbeit handelt von dem Design einer modellbasierten prädiktiven Regelung für das Thermomanagement des Batterie- und Leistungselektronikkühlkreislaufs in einem Plug-In Hybrid Fahrzeug. Der Hauptverdienst der vorgeschlagenen Lösung ist die Methodenvalidierung in einem Prototyp in Echtzeit, was häufig, wie in der Stand der Technik angeführt werden wird, einer der Mangeln der meisten Veröffentlichungen in dieser Branche ist. Um zu dieser Lösung zu gelangen, eine geeignete Modellierung und ptimierungsproblembeschreibung sowie ein effizientes und robustes numerischesWerkzeug waren wesentlich und spielen deshalb eine wichtige Rolle in diesem Dokument. Weitere angegangene Punkte bevor der Versuchsauswertung und Schlussfolgerungen, sind die Simulationsvalidierung und das Entwurf von wiederholbaren Fahrbedingungen für einen gültigen Vergleich der vorgeschlagenen Regelung gegenüber der ursprünglichen im Fahrzeug.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.