Fuel-E Cient Look-Ahead Control for Heavy-Duty Vehicles with Varying
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Fuel-E&cient Look-Ahead Control for Heavy-Duty Vehicles with Varying Velocity Demands MANNE HELD Academic Dissertation which, with due permission of the KTH Royal Institue of Technology, is submitted for public defence for the Degree of Doctor of Philosophy on Friday the 12th June 2020, at 2:00 p.m. in U1, Brinellvägen 28A, Stockholm. Doctoral Thesis in Electrical Engineering KTH Royal Institute of Technology Stockholm, Sweden 2020 © Manne Held ISBN DEFGDHGEFEIGJIDGK TRITAGEECSGAVLGPQPQ:PP Printed by: UniVersitesserVice US-AB, Sweden 2020 Abstract The fuel consumption of heavy-duty vehicles can be reduced by using information about the upcoming road section when controlling the vehicles. Most manufacturers of heavy-duty vehicles today offer such look-ahead controllers for highway driving, where the information consists of the road grade and the velocity only has small variations. This thesis considers look-ahead control for applications where the velocity of the vehicle has large variations, such as distribution vehicles or vehicles in mining applications. In such conditions, other look-ahead information is important, for instance legal speed limits and curvature. Fuel-efficient control is found by formulating and solving the driving missions as optimal control problems. First, it is shown how look-ahead information can be used to set constraints in the optimal control problems. A velocity reference from a driving cycle is modified to create an upper and a lower bound for the allowed velocity, denoted the velocity corridor. In order to prevent the solution of the optimal control problem from deviating too much from a normal way of the driving, statistics derived from data collected during live truck operation are used when formulating the constraints. It is also shown how curvature and speed limits can be used together with actuator limitations and driveability considerations to create the velocity corridor. Second, a vehicle model based on forces is used to find energy-efficient velocity control. The problem is first solved using Pontryagin’s maximum principle tofind the energy savings for different settings of the velocity corridor. The problem is then solved in a receding horizon fashion using a model predictive controller to investigate the influence of the control horizon on the energy consumption. The phasing and timing of traffic lights are then added to the available information to derive optimal control when driving in the presence of traffic lights. Third, the vehicle model is extended to include powertrain components in two different approaches. In a first approach, a Boolean variable is added to represent open or closed powertrain. This enables the vehicle to freewheel, in order to save fuel by reducing the losses due to engine drag. The problem is formulated as a mixed integer quadratic program. In a second approach, the full powertrain is modeled including a fuel map and a model of the gearbox losses, both based on measurements on real components. The problem is solved using dynamic programming, with transitions between states including gear shifts, freewheeling, and coasting in gear. Forth, the optimal control framework is used to implement an optimal control- based powertrain controller in a real Scania truck. The problem is first solved offline resulting in trajectories for velocity and freewheeling. These are used online in the vehicle as references to the existing controllers for torque and gear demands. Experiments are performed with fuel measurements, resulting in 16 % fuel savings, compared to 18 % savings by solving the optimal control problem. Sammanfattning Bränsleförbrukningen för tunga fordon kan sänkas genom att använda information om framtida vägförhållanden för att styra fordonen. De flesta fordonstillverkare erbjuder idag prediktiva farthållare för motorvägskörning, där information består av data för väglutning och fordonets hastighet endast har små variationer. Denna avhandling behandlar körfall där hastighetsvariationerna är stora, som för t.ex. fordon i distributionsdrift eller gruvfordon. För sådana fordon är andra typer av information viktiga, som t.ex. hastighetsbegränsningar och kurvatur. Genom att formulera köruppdraget som ett optimalt styrproblem, tas bränsleeffektiv styrning fram. För det första visas hur framförhållningsinformation kan användas för att sätta bivillkor i det optimala styrproblemet. Utifrån en hastighetsreferens från en körcykel skapas en hastighetskorridor, vilken består av en övre och en undre gräns för den tillåtna hastigheten. För att förhindra att hastigheten i lösning avviker för mycket från ett normalt körsätt används data från verklig lastbilskörning när bivillkoren sätts. Här visas också hur kurvatur och hastighetsbegränsningar kan användas tillsammans med begränsningar på fordonets aktuatorer och anpassning för körbarhet när hastighetskorridoren skapas. För det andra används en fordonsmodell baserad på krafter för att hitta en- ergiminimerande hastighetsstyrning. Styrproblemet löses med hjälp av Pontryagins maximum princip för att undersöka energibesparingarna för olika inställningar på hastighetskorridoren. Problemet formuleras sedan på receding-horizon form och en modellprediktiv regulator används för att undersöka horisontlängdens inverkan på energiförbrukningen. Tid och fas för trafikljus läggs sedan till den tillgängliga informationen för att hitta den optimala körstrategin vid körning bland trafikljus. För det tredje utökas fordonsmodellen till att innehålla drivlinekomponenter via två olika ansatser. I den första ansatsen används en Boleansk variabel för att repre- sentera huruvida drivlinan är öppen eller stängd. Detta gör att fordonet kan frirulla, vilket sparar bränsle genom att minska släpförlusterna i motorn. Problemet for- muleras som ett blandat kvadratiskt heltalsproblem. I den andra ansatsen modelleras hela drivlinan, med en bränslemussla för motorn och förlustmodell för växellådan baserade på tidigare mätningar. Problemet löses genom dynamisk programmering med övergångar mellan tillstånd genom växlingar, frirullning och släpning. För det fjärde används optimal styrning för att implementera en regulator för drivlinestyrning i en Scania-lastbil. Problemet löses först offline, vilket ger trajektorier för hastighet och frirullning. Dessa används sedan online i fordonet som referens till befintliga regulatorer för momentstyrning och växelval. Experiment med bränslemätning ger 16 % uppmätt bränslebesparing mot 18 % besparing från lösningen till det optimala styrproblemet. To my family Acknowledgements First of all, I would like to express my gratitude to my supervisors Oscar Flärdh at Scania and Jonas Mårtensson at KTH. Oscar, for your wide knowledge of our vehicles, for sharing your contact net, and for your great optimism. Jonas, for the guidance when writing papers and your sense for both details and the bigger picture. I would like to thank my co-supervisor Karl Henrik Johansson for your valuable inputs, and my temporal supervisor Fredrik Roos for your unprecedented expertise. I am grateful to the four managers I have had during these years: Oscar Lindström for believing in my potentials as a Ph.D. student, Mats Reimark for your visions and experience, Maria Södergren whose research I had already been following, and Lennarth Zander for sharing some of the undertakings of an industrial Ph.D. student with me. My steering committee, including my supervisors, managers, and Per Sahlholm and Anders Jensen, always pushed me far forward and gave me a lot of ideas to digest. There are many people at Scania I would like to thank for great inputs and fruitful discussions, among these are Björn Johansson, Henrik Svärd, Svante Johansson, Verena Klass, Martin Jakobsson and the colleagues in my group Powertrain Performance and Analysis. My industrial Ph.D. student colleagues Pedro Lima, Kuo-Yun Liang, Rui Oliveira, and Goncalo Pereira have been valuable in sharing experiences and for travelling with. Other KTH colleagues in collaboration with Scania that have triggered interesting discussions are Valerio Turri and Sebastian van de Hoef. The transportation group at the department have delivered great presentation and valuable feedback. The opponent of my Licentiate thesis, Jonas Fredriksson, gave me great ideas for the continuation towards this thesis. I also thank Rui Oliveira, Pedro Lima, Goncalo Pereira, and Yuchao Li for helping me improve the quality of the thesis. I would like to thank the administrators Hanna, Anneli, Gerd, Felicia, Silvia, Karin, and Christer for great support and creating a relaxed environment at the department. I thank my current and former roommates Christos Verginis, Pedro Pereira, Yulong Gao, Rong Du, Goncalo Pereira, and Pedro Roque for making our office a great room to work in. My lunch partners Robert Mattila, Alexandros Nikou, and Rodrigo Gonzalez have given me many relaxing breaks from work. The research presented in this thesis has been financed by Scania CV AB and the Swedish Governmental Agency for Innovation Systems (VINNOVA) through the FFI program. which is gratefully acknowledged. I would like to thank my parents and sisters for always being there for me and my wife Amanda for your patience, love and support. Finally, to my beloved Georg, thank you for your curiosity, ingenuity, and for teaching me new species of sharks and birds every day. Manne Held Stockholm, May 2020. Contents 1 Introduction 1 1.1 Motivation for fuel-efficient look-ahead control . .2 1.2 Fuel saving strategies . 4 1.3 Problem description .