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The E-core transverse flux machine (ETFM) has major advantages with its different and unique structure in conventional electrical machines. It is a combination of transverse flux and reluctance principle. In this work, support vector regression machines (SVRMs) are used to obtain the magnetic characteristic parameters of the ETFM for the first time and it is compared with its artificial neural network model. The data for the training and testing of the SVRMs are obtained from experimental measurements. It is proven that SVRMs can conveniently be used in the modeling of the magnetic behaviors of highly nonlinear ETFM with better accuracy and efficiency. [ABSTRACT FROM AUTHOR]

In this paper, the different working structure and unique design of the E-Core Transverse Flux Machine that is combined of transverse flux and reluctance principle is investigated for training bike application. Also, static magnetic characteristics of the machine are obtained with finite element analysis and experimental measurements. The modeling of the E-Core Transverse Flux Machine and its driver system are given. The whole system with all control algorithms for a training bike application is simulated and the performance curves are presented. Finally, the experimental tests done at the laboratory for the indoor training bike application are compared and verified with the results of the simulation. [ABSTRACT FROM AUTHOR]