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The E-core Transverse Flux Machine (ETFM) is combined of the principle of transverse flux machine and conventional Switched Reluctance Machine. The paper is focused about the modelling and the imlementation of the ETFM for an application. The magnetic characteristics of the machine determines its electrical and mechanical behaviors. To analyze and predict the ETFM's performance, good knowledge of its electromagnetic characteristics is essential. This paper investigates the use of Artificial Neural Networks (ANNs) for the modelling of the magnetic nonlinearity of the ETFM. The proposed ANN based determination of the magnetic characteristics is put into the ETFM model instead of the traditional look-up table.

In this study, modeling MCS RM (Mutually Couple S witched Reluctance Machine) which is produced through modifications in wrap around structure of S RM with Feed Forward Back Propagation ANN (Artificial Neural Network) is performed. Data obtained from angle, current, flux and torque components obtained through FEM analysis of MCS RM has been used in ANN training.In the course of literature research, no use of ANN in MCSRM modeling is detected and it is seen that algorithms consisting of analytical methods are preferred. It is established that, in modeling studies which are based on such algorithms, the structure consists of thousands of loops and that these loops extend time needed for simulation; besides, it is seen that installation of loops in modeling become rather di fficult. The data obtained from dynamic analysis of the model are compared with the data obtained from motor tests in the literature and it is witnessed that the model produces similar torques in similar voltage and current forms.

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-The E-Core Transverse Flux Machine is a different design of transverse flux machines combined with reluctance principle. Determination of the rotor position is important for the movement of the ETFM by switching the phase currents in synchronism with the inductance regions of the stator windings. It is the first time that rotor position estimation based on Artificial Neural Network (ANN) is purposed to eliminate the position sensor for the ETFM. Simulation and experimental tests are demonstrated for the feasibility of the proposed estimation algorithm for the exercise bike application of the ETFM.