Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Estimation of real traffic radiated emissions from electric vehicles in terms of the driving profile using neural networks

The increment of the use of electric vehicles leads to a worry about measuring its principal source of environmental pollution: electromagnetic emissions. Given the complexity of directly measuring vehicular radiated emissions in real traffic, the main contribution of this PhD thesis is to propose an indirect solution to estimate such type of vehicular emissions. Relating the on-road vehicular radiated emissions with the driving profile is a complicated task. This is because it is not possible to directly measure the vehicular radiated interferences in real traffic due to potential interferences from another electromagnetic wave sources. This thesis presents a microscopic artificial intelligence model based on neural networks to estimate real traffic radiated emissions of electric vehicles in terms of the driving dynamics. Instantaneous values of measured speed and calculated acceleration have been used to characterize the driving profile. Experimental electromagnetic interference tests have been carried out with a Vectrix electric motorcycle as well as Twizy electric cars in semi-anechoic chambers. Both the motorcycle and the car have been subjected to different urban and interurban driving profiles. Time Domain measurement methodology of electromagnetic radiated emissions has been adopted in this work to save the overall measurement time. The relationship between the magnetic radiated emissions of the Twizy and the corresponding speed has been very noticeable. Maximum magnetic field levels have been observed during high speed cruising in extra-urban driving and acceleration in urban environments. A comparative study of the prediction performance between various static and dynamic neural models has been introduced. The Multilayer Perceptron feedforward neural network trained with Extreme Learning Machines has achieved the best estimation results of magnetic radiated disturbances as function of instantaneous speed and acceleration. In this way, on-road magnetic radiated interferences from an electric vehicle equipped with a Global Positioning System can be estimated. This research line will allow quantify the pollutant electromagnetic emissions of electric vehicles and study new policies to preserve the environment

Multi-physics phenomena influencing the performance of the car horn

Usually cars are equipped with disk horns. In these devices electromagnetic energy is converted into mechanical energy of two nuclei that vibrate and impact each other \u2013 the impacts excite the disk that radiates sound. This paper aims at understanding the results of acoustic tests carried out on horns with different excitation voltages and different mounting brackets. Since many non-linear phenomena are inherent in the vibrations of the nuclei, a detailed model of the electromechanical system is developed. Results show the dependence of operating frequency on the input voltage and the role played by the various mechanical and electrical parameters on the dynamics of the horn. Particular nonlinear effects, like sub-harmonic excitation, are presented and discussed. A general agreement between experimental results and numerical simulations is found

Efficient and quantitative emc predictions (emission and immunity) for ECU modules

This dissertation consists of three papers. In the first paper, a methodology of building an IC model capable of predicting failures for given disturbances at the clock input based on limited or no knowledge about IC internals was developed. In the second paper, the maximized radiated emissions of the heat-sink/IC structure are predicted up to 40 GHz by creating an equivalent source using the measured electrical field in the gap between the heat-sink and ICs. The electric field is detected by an E-field probe made of an open coaxial cable coated with absorbing material. A numerical model is built in CST microwave studio to obtain the maximized radiated field with the measured field used as a source to excite the heat-sink model. The evaluated maximized radiated field is in good agreement with the measured value; the error is within 6 dB. In the third paper, a characterization method for converters with stochastic behavior is presented. The averaged and maximized spectrum of the measured voltages and currents are used to create the model. The phase information is obtained using a dedicated reference channel. After the equivalent source was determined, the actual induced noise voltage at the test load was compared to that predicted by the model with averaged and maximized spectrum to estimate its accuracy. The results indicate that the agreement with the direct measurement is within 5 dB up to 100 MHz when the load is within the characterization range --Abstract, page iv

Meta-heuristic algorithms in car engine design: a literature survey

Meta-heuristic algorithms are often inspired by natural phenomena, including the evolution of species in Darwinian natural selection theory, ant behaviors in biology, flock behaviors of some birds, and annealing in metallurgy. Due to their great potential in solving difficult optimization problems, meta-heuristic algorithms have found their way into automobile engine design. There are different optimization problems arising in different areas of car engine management including calibration, control system, fault diagnosis, and modeling. In this paper we review the state-of-the-art applications of different meta-heuristic algorithms in engine management systems. The review covers a wide range of research, including the application of meta-heuristic algorithms in engine calibration, optimizing engine control systems, engine fault diagnosis, and optimizing different parts of engines and modeling. The meta-heuristic algorithms reviewed in this paper include evolutionary algorithms, evolution strategy, evolutionary programming, genetic programming, differential evolution, estimation of distribution algorithm, ant colony optimization, particle swarm optimization, memetic algorithms, and artificial immune system

Reduction of Electric Vehicle Electromagnetic Radiations Using a Global Network Model

To address an electric vehicle’s magnetic emission problem, a model-based improvement strategy is proposed to avoid resource-intensive experimental diagnosis processes, thus achieving higher efficiency. Considering the electrical and structural characteristics of electric vehicles, a network model is developed to predict magnetic emissions. It decomposes the electronic power system into a global network and external circuit nodes according to electrical size. The Z-parameter is used to characterize the global network for the decomposition of impedance coupling so that the model parameters can be obtained separately using different methods. With this network model, an evaluation index is designed to measure the influence of technical factors on magnetic emissions by comprehensively considering their contributions and rooms for improvement. Engineers can directly determine the main interference source according to this evaluation score, and select a proper filter to attenuate the interference

Forward scatter radar for air surveillance: Characterizing the target-receiver transition from far-field to near-field regions

A generalized electromagnetic model is presented in order to predict the response of forward scatter radar (FSR) systems for air-target surveillance applications in both far-field and near-field conditions. The relevant scattering problem is tackled by developing the Helmholtz-Kirchhoff formula and Babinet's principle to express the scattered and the total fields in typical FSR configurations. To fix the distinctive features of this class of problems, our approach is applied here to metallic targets with canonical rectangular shapes illuminated by a plane wave, but the model can straightforwardly be used to account for more general scenarios. By exploiting suitable approximations, a simple analytical formulation is derived allowing us to efficiently describe the characteristics of the FSR response for a target transitioning with respect to the receiver from far-field to near-field regions. The effects of different target electrical sizes and detection distances on the received signal, as well as the impact of the trajectory of the moving object, are evaluated and discussed. All of the results are shown in terms of quantities normalized to the wavelength and can be generalized to different configurations once the carrier frequency of the FSR system is set. The range of validity of the proposed closed-form approach has been checked by means of numerical analyses, involving comparisons also with a customized implementation of a full-wave commercial CAD tool. The outcomes of this study can pave the way for significant extensions on the applicability of the FSR technique

Aeronautical Engineering: A special bibliography with indexes, supplement 62

This bibliography lists 306 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1975

Wideband and UWB antennas for wireless applications. A comprehensive review

A comprehensive review concerning the geometry, the manufacturing technologies, the materials, and the numerical techniques, adopted for the analysis and design of wideband and ultrawideband (UWB) antennas for wireless applications, is presented. Planar, printed, dielectric, and wearable antennas, achievable on laminate (rigid and flexible), and textile dielectric substrates are taken into account. The performances of small, low-profile, and dielectric resonator antennas are illustrated paying particular attention to the application areas concerning portable devices (mobile phones, tablets, glasses, laptops, wearable computers, etc.) and radio base stations. This information provides a guidance to the selection of the different antenna geometries in terms of bandwidth, gain, field polarization, time-domain response, dimensions, and materials useful for their realization and integration in modern communication systems