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Engine Fuel Consumption Modelling Using Prediction Error Identification and On-Road Data
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DOI：10.1109/TIV.2022.3167855.png)
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摘要
En 中文
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.
Keyword:
Engines
Fuels
Data models
Predictive models
Torque
Frequency modulation
Bluetooth
Engine model
prediction error identification
vehicle fuel consumption
期刊
I
IF:
14.3
论文数:
1.3K
被引数:
1.2W
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