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Application of artificial intelligence and evolutionary algorithms in simulation-based optimal design of a piezoelectric energy harvester

机译：人工智能和进化算法在压电能蓄电池仿真最优设计中的应用

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摘要

This paper tackles the problem of finding the optimal design parameters for a piezoelectric energy harvester. A new simulation-based optimization procedure is proposed with the goal of acquiring the optimal geometric and circuit design parameters that leads to higher energy harvesting efficiency and also enhances the obtained electrical power. The basis of the optimization platform is a numerical model of the energy harvesting system operating during electrical transient of charging an external storage capacitor. The model consists of a cantilever beam partially coated with piezoelectric patches, a non-linear interfacing and conditioning circuit, and a storage device. The numerical model simulates a complete energy harvesting scenario from piezoelectric transduction, to power enhancement and conditioning through interfacing circuit and energy storage. Two different case studies are considered for beams under harmonic tip-force, and harmonic base-excitation. Since performing multiple simulations in order to evaluate the objective function is computationally expensive and imposes time and space (memory) complexities, a more efficient Neural Network (NN) model is first trained based on a set of training data obtained from the numerical 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 Neural Network model for objective function evaluation. Finally, a thorough analysis of the optimal design parameters obtained from the optimization process is provided.
机译：本文解决了找到压电能源收割机的最佳设计参数的问题。提出了一种新的基于仿真优化过程，其目的是获取最佳几何和电路设计参数，这导致更高的能量收集效率，并且还增强了所获得的电力。优化平台的基础是在对外存储电容充电的电气瞬态期间操作的能量收集系统的数值模型。该模型包括部分涂覆有压电贴片的悬臂梁，非线性接口和调节电路和存储装置。数值模型模拟了通过电压转导的完整能量收集场景，通过接口电路和能量存储来提高电力增强和调节。在谐波尖端力下的梁和谐波基励磁下考虑两个不同的案例研究。由于执行多个模拟以便评估目标函数是计算昂贵的并且施加时间和空间（存储器）复杂性，因此首先基于从数值模型获得的一组训练数据训练更有效的神经网络（NN）模型。使用可用的统计方法研究了NN培训的性能和准确性。其次，遗传算法（GA）优化执行块盒优化过程，使用培训的神经网络模型进行客观函数评估。最后，提供了对从优化过程获得的最佳设计参数的彻底分析。

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