MODELING RADIATED ELECTROMAGNETIC EMISSIONS OF ELECTRIC MOTORCYCLES IN TERMS OF DRIVING PROFILE USING MLP NEURAL NETWORKS

Abstract

Current automotive electromagnetic compatibility (EMC) standards do not discuss the efiect of the driving proflle on real tra-c vehicular radiated emissions. This paper describes a modeling methodology to evaluate the radiated electromagnetic emissions of electric motorcycles in terms of the driving proflle 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 speciflc 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.