A 10-mV-Startup-Voltage Thermoelectric Energy Harvesting System Based on a Piezoelectric Cold Starter

Abstract

Traditional power batteries have been unable to meet the requirements of wireless sensor network (WSN) applications which require battery lifetime and no battery replacement. So, harvesting ambient energy and converting it into electrical energy to power wireless sensors has become an alternative to the traditional power sources. Harvesting energy from heat using thermoelectric generators (TEG) has become a prevalent topic in recent years. Since the system is required to startup with zero energy stored, the cold startup technique is necessary to activate the operation of the system in the very beginning stage without any assistance from the energy storage block, for instance, battery and capacitor. It is noted that any machines that moves, shakes, or rotates not only dissipate heat while working but also vibrate, thus the mechanical vibration can be used to startup the system. In this thesis project, a novel piezoelectric generator (PEG) startup technique is proposed to activate the system. This project focuses on pushing the minimum startup TEG voltage to an ultra-low level with the help of the PEG startup circuit.

The design of the system follows the top-down approach. Firstly, the inductive DC/DC boost converter was chosen to be the basic topology. Then, the Maximum Power Point Tracking (MPPT) technique is exploited to make sure the TEG always works at its optimal state. In addition, a Zero-current Switching (ZCS) block is proposed to regulate the synchronization of the switches and control the gate switching. Next, the state detector block monitors the state of the harvester system and instructs other control circuits to respond to the changes in the operation state. To avoid the impact of the fluctuation of supply on powering internal control circuits, a low-dropout regulator (LDO) is adopted to generate a stable supply instead. Finally, the power losses, conversion efficiency and startup principle are analyzed and calculated for performance improvement.