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OptimalSpeed Controller for a Heavy-Duty Vehicle in the Presence of SurroundingTraffic
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Optimal hastighetsregulator för ett tungt fordon i närvaro av omgivandetrafik (Swedish)
Abstract [en]

This thesis has explored the concept of an intelligent fuel-efficient speed controller for a heavy-duty vehicle, given that it is limited by a preceding vehicle. A Model Predictive Controller (MPC) has been developed together with a PI-controller as a reference controller. The MPC based controller utilizes future information about the traffic conditions such as the road topography, speed restrictions and velocity of the preceding vehicle to make fuel-efficient decisions. Simulations have been made for a so called Deterministic case, meaning that the MPC is given full information about the future traffic conditions, and a Stochastic case where the future velocity of the preceding vehicle has to be predicted. For the first case, regenerative braking as well as a simple distance dependent model for the air drag coefficient are included. For the second case three prediction models are created: two rule based models (constant velocity, constant acceleration) and one learning algorithm, a so called Nonlinear Auto Regressive eXogenous (NARX) network.

Computer simulations have been performed, on both created test cases as well as on logged data from a Scania vehicle. The developed models are finally evaluated on the test cases for both varying masses and allowed deviations from the preceding vehicle. The simulations show on a potential for fuel savings with the MPC based speed controllers both for the deterministic as well as the stochastic case.

Abstract [sv]

Denna avhandling har undersökt intelligenta och bränsleeffektiva hastighetsregulator för tunga fordon, givet ett framförvarande fordon.  En modell prediktiv kontroller (MPC), hastighetsregulator, har utvecklats tillsammans med en PI-regulator som referens. Den MPC-baserade regulatorn använder information om framtida trafikförhållanden, så som vägtopografi, hastighetsbegränsningar och hastighet hos framförvarande fordon för att ta bränsleeffektiva beslut.  Simuleringar har gjorts för ett så kallat Deterministiskt fall, vilket betyder att MPC regulatorn får fullständig information om framtida trafikförhållanden, och ett Stokastiskt fall där den framtida hastigheten hos framförvarande fordon måste predikteras. För det första fallet ingår regenerativ bromsning samt en enkel distansberoende modell för luftmotståndskoefficienten. För det andra fallet skapas tre prediktionsmodeller: två regelbaserade modeller (konstant hastighet, konstant acceleration) och en inlärningsmodell, Nonlinear Auto Regressive eXogenouse model (NARX).

Datorsimuleringar har gjorts, både på skapade testfall och på loggade data från ett Scania fordon. De utvecklade modellerna utvärderas slutligen på testfallen för både varierande massor och tillåtna avvikelser från det framförvarande fordonet. Simuleringarna visar på potential för bränslebesparingar med MPC-baserade hastighetsregulatorer både för det deterministiska och det stokastiska fallet.

Place, publisher, year, edition, pages
2018.
Series
TRITA-SCI-GRU ; 2018:250
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-229725OAI: oai:DiVA.org:kth-229725DiVA, id: diva2:1214222
External cooperation
Scania
Subject / course
Optimization and Systems Theory
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2018-06-06 Created: 2018-06-06 Last updated: 2018-06-06Bibliographically approved

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