The application of piezoelectric materials in energy harvesting devices has become an interesting topic for designers in recent years. Most of the reported works consider electrical power as a major parameter throughout the design procedure. What is less discussed is considering other design factors like the fatigue life of the harvester as well as the financial costs of the device. In this research, a methodology is introduced to design an optimum harvester, taking into account the aforementioned design factors. The finite element model of a piezoelectric harvester used in roadways is developed and validated. The fatigue test constants of the piezoelectric material are extracted via a novel approach. A parametric study is conducted on the model to generate a dataset containing electrical and mechanical characteristics, where it is used as an input for training an artificial neural network to model the behavior of the harvester. In order to evaluate the level of importance of the optimization objectives, two methods are employed: the Shannon entropy method and equal weighting factors. Results show the effectiveness of the model, where considering the electromechanical characteristics of the module is important in terms of overall performance, efficiency, durability, and financial costs.

中文翻译：

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考虑电疲劳的压电能量采集器优化

近年来，压电材料在能量收集设备中的应用已成为设计人员感兴趣的话题。大多数报告的工作都将电力视为整个设计过程中的主要参数。较少讨论的是考虑其他设计因素，例如收割机的疲劳寿命以及设备的财务成本。在这项研究中，引入了一种方法来设计最佳收割机，同时考虑到上述设计因素。开发并验证了用于道路的压电采集器的有限元模型。压电材料的疲劳测试常数是通过一种新方法提取的。对模型进行参数研究以生成包含电气和机械特性的数据集，它被用作训练人工神经网络以模拟收割机行为的输入。为了评估优化目标的重要程度，采用了两种方法：香农熵法和等权重因子。结果显示了模型的有效性，其中考虑模块的机电特性在整体性能、效率、耐用性和财务成本方面很重要。