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Engine Fuel Consumption Modelling Using Prediction Error Identification and On-Road Data

Accepted version
Peer-reviewed

Type

Article

Change log

Authors

Ainalis, D 
Cebon, D 

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.

Description

Keywords

Engines, Fuels, Data models, Predictive models, Torque, Frequency modulation, Bluetooth, Engine model, prediction error identification, vehicle fuel consumption

Journal Title

IEEE Transactions on Intelligent Vehicles

Conference Name

Journal ISSN

2379-8858
2379-8858

Volume Title

Publisher

Institute of Electrical and Electronics Engineers (IEEE)
Sponsorship
Engineering and Physical Sciences Research Council (EP/R035199/1)
Engineering and Physical Sciences Research Council (EPSRC) Grant EP/R035199/1: Centre for Sustainable Road Freight 2018-2023.