In light of the rapid growth in microelectronic technology, triboelectric nanogenerators (TENGs) have been exploited as securely sustainable substitutes for energy scavenging purposes as well as self-powered sensory utilization. In essence, TENGs’ energy output and average power distribution depend highly on certain key parameters including contact area, the thickness of electric films and external resistance. This study attempts to predict the behavior of TENGs based on variation of those key parameters and tries to optimize the associated characteristics leading to high-output and light-weight sliding-mode TENGs. To meet this problem, an artificial intelligence approach is taken into consideration and solutions for load resistance and geometry are presented. Furthermore, an experimental setup is designed to evaluate the accuracy of the simulation results, demonstrating the precision of the applied theory. The results revealed that the predefined sliding-mode TENG can harvest 0.25 mJ at each cycle in an open-circuit condition where the weight is almost 42.91 g. Moreover, simulation proves that an appropriate value for the external resistor can increase the scavenged energy up to 3.65 mJ at each reciprocal movement. Finally, temporal responses for charge, current, voltage, power output, and harvested energy are plotted and discussed, facilitating understanding of the relationship between scavenged energy and optimized parameters.
- Augmented power output
- Energy harvesting
- Optimality system
- Sliding triboelectric nanogenerators