Indoor Photovoltaic Energy Harvesting Research Articles

Enhanced power supply circuitry with long duration and high‐efficiency charging for indoor photovoltaic energy harvesting internet of things end device

AbstractLight is a popular choice as an indoor energy source for Internet of Things (IoT) end devices. However, indoor light sources are intermittent, which can disrupt the operation of IoT end devices, potentially leading to safety concerns or inaccurate data. Therefore, there is a growing need to develop a long‐duration power supply for IoT end devices. The energy from ambient light is harnessed to charge a supercapacitor through an energy manager chip. This supercapacitor serves as the power source for IoT nodes when the ambient light is unavailable. Nevertheless, as the voltage of the supercapacitor drops below the operating threshold, the IoT end node will eventually shut down. This paper proposes a circuit that utilizes a Joule Thief circuit, booster converter, and capacitor stack‐up circuit to extract the remaining energy from the supercapacitor and boost the voltage, thereby extending the operational lifespan of IoT end nodes. Additionally, capacitor stack‐up circuits significantly enhance charging efficiency. PSpice design and simulations confirm circuit feasibility. High‐efficiency charging and long‐duration IoT nodes suggest replacing traditional batteries with supercapacitors, reducing environmental impact.

Enhanced charge circuitry for indoor photovoltaic energy harvesting with fast activation and high efficiency

AbstractUsing batteries in low‐power Internet of Things (IoT) devices poses a risk of environmental pollution. Energy harvesting (EH) systems are needed to capture ambient energy and charge supercapacitors to address this issue. Indoor photovoltaic (PV) panels are a promising power source, but their weak ambient energy makes it challenging to activate IoT end nodes quickly. Here, an EH system enhanced charge circuitry with fast activation is proposed that reduces IoT end nodes activation time to less than 2 s, compared to the traditional 19.8 h, if an indoor PV panel supplying 28 µA to charge a 1 F capacity supercapacitor from 0 to 2 V. This system also offers high‐efficiency charging, enabling supercapacitors to replace batteries, particularly when charging from 0 to 1.2 V, with a charge time improvement ratio of 64%. These breakthroughs address long‐standing challenges in IoT power systems and reduce environmental pollution.

Correlation Assessment Between Power Generation by Indoor Photovoltaic Energy Harvesting and Storage Characteristics

Correlation Assessment Between Power Generation by Indoor Photovoltaic Energy Harvesting and Storage Characteristics

Online Machine Learning for 1-Day-Ahead Prediction of Indoor Photovoltaic Energy

We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of training data and analyze the sources of errors to understand the potential of indoor photovoltaic energy harvesting predictions.

Development of an Indoor Photovoltaic Energy Harvesting Module for Autonomous Sensors in Building Air Quality Applications

A 50 mm ${\times } \,\, 20$ mm ${\times } \,\, 15$ mm indoor photovoltaic (PV) energy harvesting power module (IPEHPM) has been developed for powering an Internet of Things (IoT) sensor node containing a low-power CO2 sensor for automatic building ventilation. It is composed of a high efficiency PV energy harvesting module and a supercapacitor to produce 3.6–4.2 V output voltage with 100 mA pulse current for up to 600 ms. Storage efficiency analysis and storage efficiency tests of the IPEHPM have demonstrated that with the adopted simple power management scheme, which exempts the commonly used power management blocks of the voltage regulator and the maximum power point tracking to save power, 88.7% average storage efficiency has been achieved at 200 lux. With the newly established PV powering model, the power consumption requirements of an IoT node can be directly converted into the illumination requirements of the PV energy harvester, making the IPEHPM easy to use. IPEHPM powered IoT experiments with a low-power CO2 gas sensor have demonstrated that the IPEHPM is suitable for IoT-based building ventilation applications, where the CO2 concentration level is measured every 150 s at the indoor lighting condition down to 200 lux.

On‐chip reconfigurable switched‐capacitor DC–DC converter for indoor PV energy harvesting

A reconfigurable switched-capacitor DC–DC converter is presented for indoor photovoltaic energy harvesting. A uniform hysteresis controller regulates the PV cell voltage at 1.5 V to extract the maximum power from the PV cell and regulates the load voltage at 0.9 V. To minimise the conversion loss, the energy is transferred from the PV cell to the load directly whenever energy is available in the PV cell. When the load voltage is high, the excess energy in the PV cell is stored in the buffer of 1.8 V. The load draws energy from the buffer when energy is not available in the PV cell. The proposed converter was fabricated in 0.18 μm CMOS process and achieves peak efficiencies of 87, 88 and 78% while delivering energy from the PV cell to the load, from the PV cell to the buffer and from the buffer to the load, respectively.

A measurement platform for photovoltaic performance analysis in environments with ultra-low energy harvesting potential

This paper presents a platform specially designed for high-accuracy measurement of indoor artificial lighting and environmental information for the evaluation of indoor photovoltaic energy harvesting when used in environments with ultra-low harvesting potentials with less than 1W/m2 irradiance.Components required for such platform capable of replicating and measuring different indoor environment effects are proposed. In addition to an automated plan for measurement, signaling outline, data acquisition and storage are presented. Distance alteration (50–150cm), dimming (1–100%), filtering and manipulation of incident angle (0–180°) are analyzed as light intensity modification methods, with the effect of each one on the light spectrum. Finally, some measurement results are given proceeded in the built platform.

AlGaAs Photovoltaics for Indoor Energy Harvesting in mm-Scale Wireless Sensor Nodes

Indoor photovoltaic energy harvesting is a promising candidate to power millimeter (mm)-scale systems. The theoretical efficiency and electrical performance of photovoltaics under typical indoor lighting conditions are analyzed. Commercial crystalline Si and fabricated GaAs and Al0.2Ga0.8As photovoltaic cells were experimentally measured under simulated AM 1.5 solar irradiation and indoor illumination conditions using a white phosphor light-emitting diode to study the effects of input spectra and illuminance on performance. The Al0.2Ga0.8As cells demonstrated the highest performance with a power conversion efficiency of 21%, with open-circuit voltages >0.65 V under low lighting conditions. The GaAs and Al0.2Ga0.8As cells each provide a power density of $\sim 100$ nW/mm $^{2}$ or more at 250 lx, sufficient for the perpetual operation of present-day low-power mm-scale wireless sensor nodes.