Modeling of fuel consumption using artificial neural networks
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Silesian University of Technology
Submission date: 2020-07-22
Final revision date: 2020-11-18
Acceptance date: 2020-11-18
Online publication date: 2020-11-19
Publication date: 2020-11-19
Corresponding author
Kazimierz Witaszek   

Silesian University of Technology
Diagnostyka 2020;21(4):103-113
The article presents a model of operational fuel consumption by a passenger car from the B segment, powered by a SI engine. The model was developed using artificial neural networks simulated in the Stuttgart Neural Network Simulator package. The data for the model was obtained from long-term operational tests, during which data from the engine control unit were recorded via the OBDII diagnostic interface. The model is based on neural networks with two hidden layers, the size of which was selected using an original iterative algorithm. During the structure selection process, a total of 576 different networks were tested. The analysis of the obtained test errors made it possible to select the optimal structure of the 6-19-17-1 model. The network input values were: vehicle speed and acceleration, road slope, throttle opening degree, selected gear number and engine speed. The networks were trained using the efficient RPROP method. A correctly trained network, based on the set parameters, was able to forecast the instantaneous fuel consumption. These forecasts showed a high correlation with the measured values. Average fuel consumption calculated on their basis was close to the real value, which was calculated on the basis of two consecutive fuelings of the vehicle.
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