(A) triboelectric nanogenerator and a power management module for self-driven smart tires

Abstract

A smart tire, which provides information through various sensors, is rapidly developed with autonomous vehicles to improve driving safety and reliability. However, energy supply from outside to inside tire is difficult due to the space cut and fast rotation. Thus, batteries are currently used as energy sources for these sensors, while available battery capacity is limited due to the extreme condition of the tire inside during driving. Thus, another energy source is required for smart tires to meet the enlarged energy requirement of smart tires. Therefore, this dissertation proposes triboelectric nanogenerators (TENG) and power management modules for a smart tire to supply sufficient energy for smart tires by utilizing the energy generated in the tire while driving. The first part demonstrates a TENG implemented with acoustic foam (AF), named AF-TENG, to utilize the wide inner surface of the AF as a contact surface for the triboelectric effect. With the enhanced output performance, the AF-TENG can reduce the noise generated by the tire. The second part proposes a TENG implemented by scaffold foam (SF), named SF-TENG. The SF is fabricated by using 3D printing and molding techniques. The pore’s structure of the SF can be designed with the techniques so that the output power and durability of the SF-TENG can be enhanced. In the third part, the weight of SF-TENG is reduced to less than 15 g for tire balancing. Also, long-term operation durability for 50,000 km is verified by an acceleration test. The SF-TENG's proper operation in actual driving conditions is confirmed by a real road test.The fourth part introduces a power management module (PMM) based on the switched capacitor converter (SCC). An SCC based on a parallelly connected diode array (PSCC) is demonstrated as a PMM for the SF-TENG. Compared with the previously reported fractal-structured SCC (FSCC), the PSCC shows around three times enhanced energy transferring efficiency. The last part demonstrates an energy harvesting system based on the SF-TENG and PSCC. With the smart sensor for the autonomous vehicle, the energy harvesting system installed on the inner surface of the actual tire successfully operates a sensor for the smart tire. The energy harvesting system developed in this dissertation operated smart tires for the autonomous vehicle by harvested energy. Therefore, this dissertation might give the potential as a new energy source for smart tires.