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Engine fuel consumption modelling using prediction error identification and on-road data

Engine fuel consumption modelling using prediction error identification and on-road data
Engine fuel consumption modelling using prediction error identification and on-road data
Engine 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.
Kunnappillil Madhusudhanan, Anil
0893e130-f884-42f3-a1a2-dbb5c6e32e19
Na, Xiaoxiang
508c275a-7605-4acb-9b8b-8b1d4b2f6a16
Ainalis, Daniel
db385f70-d9aa-48e5-95c0-de56bfbf3a84
Cebon, David
b004a4b5-b32f-411b-8086-0c76e51634e1
Kunnappillil Madhusudhanan, Anil
0893e130-f884-42f3-a1a2-dbb5c6e32e19
Na, Xiaoxiang
508c275a-7605-4acb-9b8b-8b1d4b2f6a16
Ainalis, Daniel
db385f70-d9aa-48e5-95c0-de56bfbf3a84
Cebon, David
b004a4b5-b32f-411b-8086-0c76e51634e1

Kunnappillil Madhusudhanan, Anil, Na, Xiaoxiang, Ainalis, Daniel and Cebon, David (2022) Engine fuel consumption modelling using prediction error identification and on-road data. IEEE Transactions on Intelligent Vehicles.

Record type: Article

Abstract

Engine 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.

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Accepted/In Press date: 12 April 2022
Published date: 19 April 2022

Identifiers

Local EPrints ID: 457356
URI: http://eprints.soton.ac.uk/id/eprint/457356
PURE UUID: 946d24e4-811c-4062-8579-85649e568087
ORCID for Anil Kunnappillil Madhusudhanan: ORCID iD orcid.org/0000-0002-0965-3767

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Date deposited: 01 Jun 2022 16:45
Last modified: 17 Mar 2024 04:07

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Contributors

Author: Xiaoxiang Na
Author: Daniel Ainalis
Author: David Cebon

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