An maximum power point tracking interface circuit for low-voltage DC-type energy harvesting sources

Abstract

This paper presents a maximum power point tracking (MPPT) interface circuit for low-voltage DC-type energy harvesting sources such as light and thermal energy. Most energy harvesting systems used in miniature-sized sensor systems require start-up circuits because the output voltages of small-sized energy transducers are very low and not enough to directly power electronic systems. The proposed interface circuit is driven directly by the low output voltages of small size energy transducers, eliminating the need for complex start-up circuitry. A simple MPPT controller with the fractional open-circuit voltage (FOCV) method is designed and fabricated in a 65-nm complementary metal oxide semiconductor (CMOS) process. Measurement results show that the designed circuit can track the MPP voltage even in the presence of the open-circuit voltage fluctuations and can operate properly at operating voltages as low as 0.3 V. The interface circuit achieves a peak power efficiency of 97.1% and an MPPT accuracy of over 98.3%.

A review of the state of art and prospects in energy storage systems for energy harvesting applications

“…In this section, the whole part of ESS is compared and analyzed from the engineering perspective and criteria to get the potential use of ESS. The most of relevant engineering parameters such as power rate, energy density, power density, response time, discharge time, round-trip efficiency energy, and lifetime, are used to evaluate the technical performance of each selected EES [51], [60], [66], [70], [76], [35]- [38]. Graphical based analyzed are presented in detail as part of evaluation of ESS performance.…”

“…Energy harvesting is a technique that captures, harvests, or scavenges various unused or ambient sources of energy e.g., sun, heat, vibration, and wind, into electrical energy [1]- [44]. Sources for energy harvesting are divided into large scales such as solar PV [6], [13], [15], [33], [39], [45], [46], wind [46]- [50], tidal [30], [51], and geothermal [35], [40], [52], [53], and then on a small scale such as electromagnetic [6], [9], [32], mechanical [19], [37], [54], [55], and heat [2], [15], [24], [56], [57]. However, in recent years, largescale energy harvesting has been more widely viewed and developed.…”

A Study on An MPPT Control Approach Using Artificial Intelligence and the Perturb and Observe Method

“…Consequently, the system generates largest power regardless of weather conditions. Researchers have developed many techniques using MPPT for optimizing PV power, including partial open-circuit voltage [6,7], and short-circuit current [6,8]. These approaches use a relation that is linear to calculate shortcircuit current and open-circuit voltage by using the maximum power point (MPP) current or voltage.…”

Optimized ANN-fuzzy MPPT controller for a stand-alone PV system under fast-changing atmospheric conditions

“…As a result, the system produces its maximum amount of power regardless of the prevailing weather conditions. To optimize PV power, researchers developed numerous MPPT algorithms, like fractional open-circuit voltage [6], [7], short-circuit current [6], [8]. These methods are based on the linear relationship between the maximum power point (MPP) current or voltage and the corresponding values for the short-circuit current and open-circuit voltage.…”