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Transducers based on dielectric electroactive polymers (DEAP) use electrostatic pressure to convert electrical into mechanical energy or vice versa. To scale up the actuation or the energy gain, multilayer transducers like DEAP stack transducers are appropriate. Within this contribution, a model of such a stack transducer is derived and experimentally validated. The model is based on a multi-domain approach to describe the mechanical dynamics and the electrical behavior of the DEAP. Since these two domains influence each other they are coupled afterwards by a novel approach using interchanging power flows. To parametrize this model, tensile and compression tests for different polymer materials were performed under static and transient considerations. The results of these experiments show that the parameters obtained from the tensile test sufficiently describe the compression mode and can therefore be used for the model. Based on this transducer model the overall energy and the different parts of the multi-domain are analytically determined for arbitrary operating points. These expressions for the energies are finally used to optimize well-defined coupling coefficients, by which a maximum part of the electrical input energy is converted to mechanical energy, especially mechanical work.

Transducers based on dielectric electroactive polymers (DEAP) use electrostatic pressure to convert electric energy into strain energy or vice versa. Besides this, they are also designed for sensor applications in monitoring the actual stretch state on the basis of the deformation dependent capacitive-resistive behavior of the DEAP. In order to enable an efficient and proper closed loop control operation of these transducers, e.g. in positioning or energy harvesting applications, on the one hand, sensors based on DEAP material can be integrated into the transducers and evaluated externally, and on the other hand, the transducer itself can be used as a sensor, also in terms of self-sensing. For this purpose the characteristic electrical behavior of the transducer has to be evaluated in order to determine the mechanical state. Also, adequate online identification algorithms with sufficient accuracy and dynamics are required, independent from the sensor concept utilized, in order to determine the electrical DEAP parameters in real time. Therefore, in this contribution, algorithms are developed in the frequency domain for identifications of the capacitance as well as the electrode and polymer resistance of a DEAP, which are validated by measurements. These algorithms are designed for self-sensing applications, especially if the power electronics utilized is operated at a constant switching frequency, and parasitic harmonic oscillations are induced besides the desired DC value. These oscillations can be used for the online identification, so an additional superimposed excitation is no longer necessary. For this purpose a dual active bridge (DAB) is introduced to drive the DEAP transducer. The capabilities of the real-time identification algorithm in combination with the DAB are presented in detail and discussed, finally.

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Energy harvesting using dielectric elastomers is an upcoming possibility to convert ambient energy into electric energy. Published results for energy harvesting cycles deal with charging and discharging of the polymer during a constant stretch state. However, real applications feature a continuously changing stretch and thus the time frames of the charging- and discharging-intervals have a considerable influence on the amount of harvested energy. This paper presents the calculation of the optimal charging- and discharging-intervals to maximize the energy gain. For this purpose the authors investigate the physical model of a lossy generator to derive the converted energy as a function of the timing of the charging- and discharging-interval. The subsequent optimization results in an energy-optimal harvesting cycle that combines the fundamental harvesting cycles with constant electric field and constant charge. Finally, we present the achievable energy gain of this optimized harvesting cycle as well as control laws to realize the optimized harvesting cycle.