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

Wefky, Ahmed; Espinosa, Felipe; Santiago, Luis de; Gardel, Alfredo; Revenga, P.; Martinez, M. Comesana

doi:10.2528/pier12102510

Cited by 10 publications

(7 citation statements)

References 29 publications

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“…For that reason, an alternative approach based on artificial neural networks (ANNs) has been proposed in this paper. Neural networks are very convenient as a modeling tool since they have the ability to learn from presented data [17][18][19][20][21]. Compared to conventional signal processing algorithms that are mainly based on linear models, neural networks consider DOA estimation as approximation of highly nonlinear multidimensional function, or in other words, as a mapping between spatial covariance matrix of received signals from antenna elements and DOAs.…”

Section: Introductionmentioning

confidence: 99%

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Agatonovic¹,

Stankovic²,

Milovanović³

et al. 2013

PIER

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Abstract-In this paper, we present an efficient Artificial Neural Network (ANN)-based model to estimate both azimuth and elevation arrival angles of a signal source. To achieve this goal, the ANN model is constructed using measurement data obtained by a rectangular antenna array in the space-frequency domain. Unlike classical superresolution algorithms such as 2D MUSIC, the proposed model is capable to account for imperfections of measurement equipment as well as mutual couplings between array elements. The neural model has been verified for several angular positions and frequencies. It is shown that the use of ANN model to estimate angular positions of a signal source yields more accurate results when compared to 2D MUSIC. Moreover, the neural model significantly outperforms 2D MUSIC in terms of speed of computation.

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Section: Introductionmentioning

confidence: 99%

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Agatonovic¹,

Stankovic²,

Milovanović³

et al. 2013

PIER

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“…In order to bypass the difficulties of inverting a numerical model to estimate these parameters starting from EC data [18], a model-free estimation technique based on the use of an ANN is considered. This kind of approach has been proven to be efficient in various modeling and estimation problems in the electromagnetic domain [19,20]. In this study, a feed-forward neural network (FFNN) is implemented to estimate the conductivity variations [21].…”

Section: Evaluation Of Conductivity Variation Of Aluminum and Solder mentioning

confidence: 99%

Estimation of the Ageing of Metallic Layers in Power Semiconductor Modules Using the Eddy Current Method and Artificial Neural Networks

Nguyen¹,

Joubert²,

Lefebvre³

2014

PIER M

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Abstract-In high power operations, the ageing of power semiconductor modules has been often observed by several failures due to high temperature cycling. The main failures may be metallization reconstruction, solder delaminations, bond wire lift-offs or bond wire heel crackings, conchoidal breaking of ceramics. The paper focuses on the non-contact monitoring of the ageing of the aluminum metallization top layer and of the solder bottom layer of a power die, using the eddy current method. The ageing is assumed to induce a decrease of these layers conductivity. The evaluation of both layers conductivity changes are estimated using artificial neural networks starting from eddy current data provided by finite element computations carried out in the case of several aged die configurations. The error of estimation is less than a few percent in the considered cases and it demonstrates the relevance of the eddy current method to monitor the ageing state of power modules. The proposed approach provides relevant results which will be validated on experimental data in future works.

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“…Furthermore, such antenna would receive a lot of radiated waves from various interfering sources like: other vehicles, WiFi, GPS, Bluetooth, television broadcast, radio broadcast, mobile networks, satellite networks, and power lines. In previous articles [17,18], authors have already proposed a procedure of three steps to solve this problem as shown in Figure 1. In this paper, the same methodology has been applied on a Renault Twizy and a Think City.…”

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

Cited by 10 publications

References 29 publications

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Efficient Neural Network Approach for 2d Doa Estimation Based on Antenna Array Measurements

Estimation of the Ageing of Metallic Layers in Power Semiconductor Modules Using the Eddy Current Method and Artificial Neural Networks

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

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