Engine Fuel Consumption Modelling using Prediction Error Identification and On-road Data
IEEE Transactions on Intelligent Vehicles
Institute of Electrical and Electronics Engineers (IEEE)
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Madhusudhanan, A., Na, X., Ainalis, D., & Cebon, D. (2022). Engine Fuel Consumption Modelling using Prediction Error Identification and On-road Data. IEEE Transactions on Intelligent Vehicles, 1-1. https://doi.org/10.1109/TIV.2022.3167855
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.
Engineering and Physical Sciences Research Council (EPSRC) Grant EP/R035199/1: Centre for Sustainable Road Freight 2018-2023.
Engineering and Physical Sciences Research Council (EP/R035199/1)
External DOI: https://doi.org/10.1109/TIV.2022.3167855
This record's URL: https://www.repository.cam.ac.uk/handle/1810/336435
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