Energy Harvesting and Power Optimization for Remote Sensing Systems

Energy harvesting for electronic devices such as wireless sensor nodes poses challenges on the design of circuits for harnessing, conditioning, transferring power, and storing energy. For long durability, we consider the use of supercapacitors instead of batteries. To maximize the efficiency of micro-harvesters and to sustain extended periods of poor weather, we propose to generalize maximum power point transfer tracking (MPTT) to MCZT, for Maximum Charging Zone Tracking, to expand the zones of effective charging. We adopt a programmable charge pump driven by a direct digital synthesizer (DDS) to cover the wide dynamic range of solar irradiation. Furthermore, to address the problems of high leakage and high unusable residual charge of supercapacitors-based Energy Storage Element (ESE), we propose two methods: selection of optimal sizes of the supercapacitors at design time, and choosing the optimal series vs. parallel topology at run time. These techniques for the micro-harvester have been evaluated by simulations and measurement and validated with several fully implemented embedded systems.;In addition, power optimization schemes for locally daisy-chained sensing systems are proposed. Such a system consists of a data aggregator with a data uplink and distributes power over cables on the order of 10s of meters to one or more daisy-chained nodes that perform sensing and transmit the data back to the data aggregator. We reduce power consumption at two levels. First, instead of keeping the power lines on for the entire time, we transmit power at a higher voltage to increase the efficiency of power delivery. Second, we add supercapacitors to the sensing nodes so that they can be charged quickly during sleep mode and power the nodes when the peak current is required in active mode, as well as most time of sleep mode, thereby minimizing transmission loss. The data aggregator can even go into power-down mode and be waken up by sensing nodes upon event detection by varying the power lines voltage. Experimental results show that our proposed techniques significantly reduce power consumption.