학술논문
Engine Fuel Consumption Modelling Using Prediction Error Identification and On-Road Data
Document Type
Periodical
Author
Source
IEEE Transactions on Intelligent Vehicles IEEE Trans. Intell. Veh. Intelligent Vehicles, IEEE Transactions on. 8(2):1392-1402 Feb, 2023
Subject
Language
ISSN
2379-8858
2379-8904
2379-8904
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.