| Title: |
Engine fuel consumption modelling using prediction error identification and on-road data |
| Authors: |
Madhusudhanan, Anil K.; Na, Xiaoxiang; Ainalis, Daniel; Cebon, David |
| Publication Year: |
2023 |
| Collection: |
University of Southampton: e-Prints Soton |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://eprints.soton.ac.uk/457356/1/bare_jrnl.pdf; https://eprints.soton.ac.uk/457356/2/Engine_Fuel_Consumption_Modelling_using_Prediction_Error_Identification_and_On_road_Data_1_.pdf; Madhusudhanan, Anil K., Na, Xiaoxiang, Ainalis, Daniel and Cebon, David (2023) Engine fuel consumption modelling using prediction error identification and on-road data. IEEE Transactions on Intelligent Vehicles, 8 (2), 1392-1402. (doi:10.1109/TIV.2022.3167855 ). |
| Availability: |
https://eprints.soton.ac.uk/457356/; https://eprints.soton.ac.uk/457356/1/bare_jrnl.pdf; https://eprints.soton.ac.uk/457356/2/Engine_Fuel_Consumption_Modelling_using_Prediction_Error_Identification_and_On_road_Data_1_.pdf |
| Rights: |
cc_by_4 |
| Accession Number: |
edsbas.93CB8FBC |
| Database: |
BASE |