The optimization of an electromagnetic vibration energy harvester based on developed electromagnetic damping models

The optimization of electromagnetic energy harvesters is the main goal of this work to enhance power generation performance. The optimization procedure requires a flexible computational framework to predict the characteristics of various electromagnetic energy harvesters with different architectures. The electromagnetic damping is one of the challenging topics in this field, which depends on the architecture of energy harvester, base excitation condition, and electrical resistance. In this regard, two innovative computational models were developed to predict electromagnetic damping without any dependency on experimental results. The results showed that the developed models are 270 times faster than traditional approaches such as the finite element method. The developed semi-analytical framework was implemented in the optimization procedure to enhance energy harvester performance. The optimization determined optimal characteristics for control parameters, including the number of coils turns, the configuration of winding coil, and magnets dimensions. Moreover, a prototype was constructed based on optimal characteristics derived from the optimization. The developed models were validated by experimental results and the FEM method. The experimental results indicated that the optimization presented in the current study led to significant improvements in the outputs of the optimal harvester compared to previous studies. These outputs include power 999.7 mW, normalized power 129.15 mW/g(2), and power density 2.16 mW/cm(3), to name a few. In addition, the middle coil measures environmental vibration as a self powered sensor, which can be used for condition monitoring and self-tuning. These achievements make the optimized energy harvester exceptionally suitable for various applications in large-scale systems.

Automated summary

The main goal of this research is to optimize electromagnetic energy harvesters, using a flexible computational framework to predict characteristics, and developing innovative computational models to improve optimization speed and performance. Experimental results validate the effectiveness of the optimization, with the optimized energy harvester demonstrating excellent power performance and self-powered sensor function.