This paper presents an advanced study including the design, characterization and theoretical analysis of a capacitive vibration energy harvester. Although based on a resonant electromechanical device, it is intended for operation in a wide frequency band due to the combination of stop-end effects and a strong biasing electrical field. The electrostatic transducer has an interdigited comb geometry with in-plane motion, and is obtained through a simple batch process using two masks. A continuous conditioning circuit is used for the characterization of the transducer. A nonlinear model of the coupled system ‘transduce-conditioning circuit’ is presented and analyzed employing two different semi-analytical techniques together with precise numerical modelling. Experimental results are in good agreement with results obtained from numerical modelling. With the 1 g amplitude of harmonic external acceleration at atmospheric pressure, the system transducer-conditioning circuit has a half-power bandwidth of more than 30% and converts more than 2 µW of the power of input mechanical vibrations over the range of 140 and 160 Hz. The harvester has also been characterized under stochastic noise-like input vibrations.

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International audienceIn this paper, we present a formal analysis and description of the steady-state behavior of an electrostatic vibration energy harvester operating in constant-charge mode and using different types of electromechanical transducers. The method predicts parameter values required to start oscillations, allows a study of the dynamics of the transient process, and provides a rigorous description of the system, necessary for further investigation of the related nonlinear phenomena and for the optimisation of converted power. We show how the system can be presented as a nonlinear oscillator and be analysed by the multiple scales method, a type of perturbation technique. We analyse two the most common cases of the transducer geometry and find the amplitude and the phase of steady-state oscillations as functions of parameters. The analytical predictions are shown to be in good agreement with the results obtained by behavioral modeling