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Modeling of the air conditions effects on the power and fuel consumption of the SI engine using neural networks and regression

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Abstract

Engine performance varies significantly due to the variations in weather conditions in different regions. So, in order to optimize the performance and fuel consumption, engines should be calibrated according to the weather conditions in which they operate. In this paper the effects of the air conditions (such as pressure and temperature) on the power and fuel consumption of the SI engine are modeled. First a comprehensive one-dimensional model of the real engine is constructed in GT POWER®, and validated with experimental data from actual engine. Next, using this model, a set of experiments is carried out by varying pressure, temperature, and humidity of the incoming air, and engine speed. The measuring outputs are the power and BSFC of the engine. Then, two mathematical models are developed using MLP Neural Networks and also regression technique to estimate the outputs in terms of the inputs. At last, the estimation ability of the models is shown by a set of new experiments. These models could be used in engine calibration and shift the process from a near blind one to the one in which prior information have a significant role.

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Notes

  1. Computer experiment.

Abbreviations

u :

Input variable

p :

Pressure

T :

Temperature

Wi :

Weight of ith layer

Wij :

Neuron weight corresponding to jth input

b i :

Bias of the ith neuron

b :

Network bias

BSFC:

Brake Specific Fuel Consumption

RPM:

Engine speed

SI:

Spark ignition

a0, a1, a2, b0, b1, b2, c0, c1, c2, d0, d1, d2 :

Constants coefficient

rms:

Root mean square

x :

Real value

\(\hat{x}\) :

Predicted value

N :

Number of data samples

Pw :

Power

IC :

Internal combustion

φ :

Activation function

n :

Number of neurons in hidden layer

0:

Based amount

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Authors and Affiliations

  1. Technical and Vocational University of Iran, Behshahr Imam Khomeini Branch, Behshahr, Mazandaran, Iran

    Mohammad Rahimi-Gorji

  2. Iran University of Science and Technology, Department of Automotive Engineering, Narmak, Tehran, Iran

    Mostafa Ghajar & Amir-Hasan Kakaee

  3. Babol Noshirvani University of Technology, Department of Mechanical Engineering, Babol, Mazandaran, Iran

    Mohammad Rahimi-Gorji & Davood Domiri Ganji

Authors
  1. Mohammad Rahimi-Gorji
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  2. Mostafa Ghajar
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  3. Amir-Hasan Kakaee
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  4. Davood Domiri Ganji
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Corresponding author

Correspondence to Mohammad Rahimi-Gorji.

Additional information

Technical Editor: Luis Fernando Figueira da Silva.

Appendix

Appendix

See Table 8.

Table 8 Coefficients of the regression model of the BSFC and power
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Rahimi-Gorji, M., Ghajar, M., Kakaee, AH. et al. Modeling of the air conditions effects on the power and fuel consumption of the SI engine using neural networks and regression. J Braz. Soc. Mech. Sci. Eng. 39, 375–384 (2017). https://doi.org/10.1007/s40430-016-0539-1

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  • DOI: https://doi.org/10.1007/s40430-016-0539-1

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