Power Transfer Optimization for Triboelectric Nanogenerators

We study the energy harvesting from a triboelectric nanogenerator (TENG) and present a method for optimization of the power delivered to the load. The variable internal capacitance in the TENG model introduces challenge in the design of the loading circuit. Additionally, the transferred power depends on the frequency of the external force and the parasitic capacitor at the load. This calls for adaptive loading and implementation of maximum power point tracking(MPPT) algorithms. We investigate strategies for MPPT in the case of pure resistive load and with additional parallel capacitive load. We demonstrate that when the capacitive load is taken into account, a significant gain in the maximum power delivered to the load can be achieved. We derive the ratio of the output voltage and open-circuit voltage at the generator for the optimal power transfer when the parallel capacitance is present.