Piezoelectric energy harvesting: modeling, optimization, and experimental study of transient charging behavior

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Bagheri, Shahriar
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Piezoelectric Energy Harvesters (PEH) are complex dynamic electromechanical systems. As such, derivation of an accurate model that can describe system's behavior under different operating conditions is challenging. Moreover, the interconnection between the piezoelectric transducer and any forms of power conditioning and storage further complicates the modeling process. This thesis tackles the problem of modeling the transient operation of a PEH during the charging process of an external storage device through electrical interfacing circuits. A semi-theoretical model is proposed based on the Euler-Bernoulli beam theory. The model takes into account the electromechanical coupling effects of the piezoelectric material, as well as the dynamic process of charging an external storage capacitor. The effects of an standard interfacing circuit with diode bridge rectifier and a non-linear synchronous switching circuit on the transient charging dynamics are modeled and comprehensively studied. Additionally, an experimental test setup is developed to validate the efficacy of the developed model and to further investigate the effect of different interfacing circuits on the energy harvesting system. Furthermore, the problem of finding the optimal design parameters for a PEH is considered. A new simulation-based optimization procedure is proposed with the goal of acquiring the optimal geometric and circuit design parameters that lead to higher energy harvesting efficiency and also enhance the obtained electrical power. The basis of the optimization platform is the developed semi-theoretical model of the energy harvesting system. In order to avoid the time and space (memory) complexities during the computer optimization caused by the expensive-to-evaluate Objective Function (OF) (i.e. simulation model) combination of Artificial Intelligence (AI) and Evolutionary Algorithm (EA) is used to facilitate the optimization process, while maintaining the required accuracy. More precisely, a computationally efficient Neural Network (NN) model is first trained based on a set of training data obtained from the simulation model. Performance and accuracy of the NN training is studied using available statistical methods. Second, a Genetic Algorithm (GA) optimization performs a block-box optimization procedure, using the trained NN model for OF evaluation. Finally a thorough analysis of the optimal design parameters obtained from the optimization process is provided.
Energy harvesting, Piezoelectric, Modeling, Optimization