Change search
ReferencesLink to record
Permanent link

Direct link
Preceding Vehicle Dynamics Modeling for Fuel Efficient Control Strategies
KTH, School of Electrical Engineering (EES).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Long haulage trucks are a key part of today’s goods trans-port networks. To reduce fuel costs and emissions from trucks, novel methods of regulating their speed optimally based on road slope data and other vehicles’ behavior are being developed. An important ability for these systems, when there is no vehicle to vehicle communication, is to be able to anticipate the speed of the vehicle driving in front.This master thesis explores a number of possible ap-proaches of predicting the speed of a preceding heavy ve-hicle. The work is limited to vehicles controlled by one of two common speed control systems: cruise control (CC) and look ahead cruise control (LACC) when driving on a highway. Initially, general methods of grey box and black box modeling are used. These are then refined into more specialized predictors that combine rule based algorithms with grey box or black box models.The speed controllers are found to have highly nonlinear switching behavior, making them diÿcult to predict. The general methods were found to either produce inaccurate predictions or require unacceptably large amounts of train-ing data. The two developed methods, one using switched ARX models and the other using switched grey box mod-els, required little training data and produced satisfactory results. The presented switched grey box model approach results in a 2 % reduction in fuel consumption relative to the naive assumption that the speed of the leading vehicle will remain the same over the prediction horizon.

Abstract [sv]

Långtradare är en viktig del av dagens transportnät. I syfte att minska bränslekostnader och utsläpp från långtradare utvecklar man nya metoder av att optimalt reglera has-tigheten med avseende på andra fordon och väglutning. I fallet då det inte finns någon kommunikation mellan for-donen, en viktig funktion för dessa system blir att kunna förustäga hastigheten for det framförvarande fordonet.Detta exjobb utforskar ett antal möjliga prediktorer för hastigheten av ett fordon som kör framför det egna. Man väljer att begränsa sig till två vanliga regulatorer för last-bilar som kör på motorväg: ordinarie farthållare (CC) och farthållare med topografisk planering (LACC). Till en bör-jan används allmänna metoder för modellering av system som grå- och svartlådemodeller. Dessa fick sedan utvecklas till mer specialiserade prediktorer som kombinerar regelba-serade algoritmer med grå- och svartlådemodeller.Hastighetsregulatorerna har starkt olinjärt beteende. Detta gör dem svårpredikterade. De allmänna metoderna gav antingen resultat med låg noggrannhet eller krävde sto-ra mängder träningsdata. Två metoder utvecklades. Den ena använder switchade ARX modeller medan den andra använder switchade grålådemodeller. Metoderna behöver bara små mängder träningsdata och ger bra prediktioner. Metoden med switchade grålådemodeller minskade bräns-leförbrukningen med 2 % relativt antagandet att fordonets hastighet kommer förbli densamma under hela prediktions-horisonten.

Place, publisher, year, edition, pages
2016. , 73 p.
Series
TRITA-EE, ISSN 1653-5146 ; TRITA -EE 2016:122
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-196861OAI: oai:DiVA.org:kth-196861DiVA: diva2:1049394
External cooperation
Scania CV AB
Examiners
Available from: 2016-11-24 Created: 2016-11-24 Last updated: 2016-11-24Bibliographically approved

Open Access in DiVA

fulltext(6528 kB)13 downloads
File information
File name FULLTEXT01.pdfFile size 6528 kBChecksum SHA-512
db132c44779939c7ef44790c5f32375319e6d093b15346d4bae0e28e5c315db03d28ab8878f0e96333881691676c7cff6b6f11a7f8e04d8cf2dfbe25a6e5fdfc
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering (EES)
Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 13 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Total: 8 hits
ReferencesLink to record
Permanent link

Direct link