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Methodology of determining basic technical parameters of electric-drive car

Kozłowski M., Tomczuk K., Szczypior J.

Przegląd Elektrotechniczny

2011

R. 87, nr 10

299-304

The article presents issues of electric construction of a moving car prototype which enables transport of the disabled. Methodology has been presented of determining required traction characteristics of an electric-drive car on the basis of analyses of object model driving graphs for the assumed reliable traffic conditions. Optimization method has been used for determining nominal parameters of the main devices. The condition of minimization of energy losses of a drive system has been assumed as a solution criterion.

W artykule omówiono problemy elektryczne budowy jeżdżącego prototypu samochodu, umożliwiającego przewóz osób niepełnosprawnych. Przedstawiono metodykę wyznaczania wymaganych charakterystyk trakcyjnych samochodu elektrycznego na podstawie analiz przebiegów jazdy modelu obiektu dla zakładanych miarodajnych warunków ruchu. Do wyznaczania parametrów znamionowych urządzeń głównych zastosowano metodę optymalizacji. Jako kryterium rozwiązania przyjęto warunek minimalizacji strat energii układu napędu.

The correction of nominal parameters of electronic circuits based on selected unidirectional and recurrent artificial neural networks (ANN)

Józefowicz K., Rybarczyk A.

Poznan University of Technology Academic Journals. Electrical Engineering

2008

No 58

41-52

In this paper the suggestion how to use artificial neural networks (ANN) and interactive environment MATLAB for correction of nominal parameters of active filters of higher order with a simultaneous process of optimization of the designed project [8,9] has been presented as well as the possibilities provided by the package MATLAB (NNT) [3, 15] as to quick design and testing methods of the algorithms of artificial neural networks and the verification of statistical properties of the acquired project. Thus, the paper focuses on the idea how both unidirectional and recurrent artificial neural circuits might aid in designing electronic circuits with the correction of the parameters' nominal point. The aim of the analysis was to build the neural optimizer used to acquire such values of constructional parameters of the network examined which will minimize the variance of selected output values, in other words - minimize the dependence of design parameters on dispersion of technological parameters [10-18] With regard to each type of network studied, a relevant method of learning has been implemented. A few absolutely impressive solutions of neural optimizers for a dedicated project task have been presented, their qualities compared and the advantages resulting from the application of ANN, in this particular case towards the other methods of optimization, have been pointed out.