Monitoring and Analysis of Magnetic Fields Onboard Transport Systems: Waveforms and Exposure Assessment

Kopytenko, Yuri A.; Птицына, Н. Г.; Tyasto, M. I.; Ismaguilov, V.; Villoresi, G.

doi:10.1109/emceco.2007.4371725

Cited by 3 publications

(1 citation statement)

References 2 publications

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“…Most researchers interested in the measurement and analysis of electromagnetic emissions from electric driven vehicles concentrated on the magnetic field data [15,[39][40][41][42][43]. Furthermore, recent measurements of electromagnetic emissions from a hybrid vehicle showed that the level of the electric radiated disturbances has been below the limits established by the CISPR 25 [12].…”

Section: Introductionmentioning

confidence: 99%

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Wefky¹,

Espinosa²,

Leferink³

et al. 2013

PIER

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Abstract-This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.

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