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dc.contributor.authorMadhusudhanan, AK
dc.contributor.authorNa, X
dc.contributor.authorAinalis, D
dc.contributor.authorCebon, D
dc.date.accessioned2022-04-25T23:30:28Z
dc.date.available2022-04-25T23:30:28Z
dc.date.issued2022
dc.identifier.issn2379-8858
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/336435
dc.description.abstractEngine modelling is an important step in predicting the fuel consumption of a vehicle. Existing methods in the literature require dedicated tests on a test track or on a chassis dynamometer or they require measurements from several days of vehicle operation. This article proposes a new method to model fuel flow rate of a diesel engine and a compressed gas engine using prediction error identification and on-road data collection. The model inputs are the engine torque and speed. The on-road vehicle data was collected during normal transport operations. The identification data set was approximately 99% shorter than the baseline method. The proposed method is applicable for other types of vehicles, including electric vehicles. The identified engine models have less than 1.3% mean error and 2.5% RMS error.
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC) Grant EP/R035199/1: Centre for Sustainable Road Freight 2018-2023.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved
dc.titleEngine Fuel Consumption Modelling using Prediction Error Identification and On-road Data
dc.typeArticle
dc.publisher.departmentDepartment of Engineering
dc.date.updated2022-04-25T13:49:57Z
prism.endingPage1
prism.publicationDate2022
prism.publicationNameIEEE Transactions on Intelligent Vehicles
prism.startingPage1
dc.identifier.doi10.17863/CAM.83852
rioxxterms.versionofrecord10.1109/TIV.2022.3167855
rioxxterms.versionAM
dc.contributor.orcidNa, Xiaoxiang [0000-0002-6524-7122]
dc.contributor.orcidCebon, David [0000-0003-2828-6445]
dc.identifier.eissn2379-8858
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/R035199/1)
cam.issuedOnline2022-04-19
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cam.depositDate2022-04-25
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rioxxterms.freetoread.startdate2022-04-19


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