volume | PIER Journals

2012-12-20

Modeling Radiated Electromagnetic Emissions of Electric Motorcycles in Terms of Driving Profile Using Mlp Neural Networks

Ahmed Wefky,

Mr. Ahmed Wefky

Department of Electronics

University of Alcala

Spain

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Felipe Espinosa,

Dr. Felipe Espinosa

Department of Electronics

University of Alcala

Spain

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Luis de Santiago,

Dr. Luis de Santiago

Department of Electronics

University of Alcala

Spain

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Alfredo Gardel,

Dr. Alfredo Gardel

Department of Electronics

University of Alcala

Spain

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Pedro Revenga,

Dr. Pedro Revenga

Department of Electronics

University of Alcala

Spain

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Miguel Martinez

Dr. Miguel Martinez

Department of Electronics

University of Alcala

Spain

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Progress In Electromagnetics Research, Vol. 135, 231-244, 2013

doi:10.2528/PIER12102510

Abstract

Current automotive electromagnetic compatibility (EMC) standards do not discuss the effect of the driving profile on real traffic vehicular radiated emissions. This paper describes a modeling methodology to evaluate the radiated electromagnetic emissions of electric motorcycles in terms of the driving profile signals such as the vehicle velocity remotely controlled by means of a CAN bus. A time domain EMI measurement system has been used to measure the temporal evolution of the radiated emissions in a semi-anechoic chamber. The CAN bus noise has been reduced by means of adaptive frequency domain cancellation techniques. Experimental results demonstrate that there is a temporal relationship between the motorcycle velocity and the radiated emission power in some specific frequency ranges. A Multilayer Perceptron (MLP) neural model has been developed to estimate the radiated emissions power in terms of the motorcycle velocity. Details of the training and testing of the developed neural estimator are described.

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Ahmed Wefky, Felipe Espinosa, Luis de Santiago, Alfredo Gardel, Pedro Revenga, and Miguel Martinez, "Modeling Radiated Electromagnetic Emissions of Electric Motorcycles in Terms of Driving Profile Using Mlp Neural Networks," Progress In Electromagnetics Research, Vol. 135, 231-244, 2013.
doi:10.2528/PIER12102510

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