Recently Magnetostrictive materials have been proposed as active materials to be used in several energy harvesting technology [1]. In this kind of application, the working condition of the material is highly dynamic and non linear.udAs a result static models of magnetostrictive materials are usually not very accurate and can be not reliable to develop a sufficiently accurate designof the energy harvesting devices. The presence of hysteresis requires accurateudmathematical modeling in order to correctly foresee the behavior of real materials (ferromagnetic or magnetostrictive) used in control systems or inudelectrical machines and thus simplifying the design of such controllers or predicting with acceptable accuracy electromagnetic fields in suchuddevices[2]. In order to overcome this problem, this paper addresses the development of Dynamic Preisach hysteresis model (DPM) for magnetostrictive materials for energy application operating in hysteretic and time varying nonlinearudregimes. DPM is a development of classical Preisach Model which is able to include dynamical features in the mathematical model of hysteresis.udIn this paper the magnetostrictive material considered is Terfenol-D. Its hysteresis is modeled by applying the DPM whose identification procedure is performed by using a neural network procedure previously publised [3]. Theudneural network used is a multiplayer perceptron trained with the Levenberg-Marquadt training algorithm. This allows to obtain both Everett integrals and the Preisach distribution function, without any special conditioning of the measured data, owing to the filtering capabilities of the neural network interpolators.udThe model is able to reconstruct both the magnetization relation and the Field-strain relation. The model is validated through comparison and prediction of data collected from a typical Terfenol-D transducer.
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