Maria Teresa Penella

A New MPPT Method for Low-Power Solar Energy Harvesting

This paper describes a new maximum-power-point-tracking (MPPT) method focused on low-power (<; 1 W) photovoltaic (PV) panels. The static and dynamic performance is theoretically analyzed, and design criteria are provided. A prototype was implemented with a 500-mW PV panel, a commercial boost converter, and low-power components for the MPPT controller. Laboratory measurements were performed to assess the effectiveness of the proposed method. Tracking efficiency was higher than 99.6%. The overall efficiency was higher than 92% for a PV panel power higher than 100 mW. This is, in part, feasible due to the low power consumption of the MPPT controller, which was kept lower than 350 μW. The time response of the tracking circuit was tested to be around 1 s. Field measurements showed energy gains higher than 10.3% with respect to a direct-coupled solution for an ambient temperature of 26°C. Higher gains are expected for lower temperatures.

A Review of Commercial Energy Harvesters for Autonomous Sensors

Current commercial autonomous sensors are mainly powered by primary batteries. Batteries need to be replaced and hence can become the largest and most expensive part of the system. On the other hand, our environment is full of waste and unused energy such as that coming from the sun or mechanical vibrations. As a result, commercial energy harvesters are increasingly available to power autonomous sensors. This work presents and analyses commercial energy harvesters currently available. First, environmental energy sources are classified and described. Then, energy harvesting principles are described and some guidelines are given to calculate the maximum power consumption allowed and the energy storage capacity required for the autonomous sensor. Finally, commercial energy harvesters are evaluated to determine their capability to power a commercial autonomous sensor in some given circumstances.

A Closed-Loop Maximum Power Point Tracker for Subwatt Photovoltaic Panels

This paper proposes a closed-loop maximum power point tracker (MPPT) for subwatt photovoltaic (PV) panels used in wireless sensor networks. Both high power efficiency and low circuit complexity are achieved. A microcontroller (μC) driven by a fast clock was used to implement an MPPT algorithm with a low processing time. This leads to a maximum central-processing-unit duty cycle of 6% and frees the μC to be used in the remaining tasks of the autonomous sensor, such as sensing, processing, and transmitting data. In order to reduce power consumption, dynamic power management techniques were applied, which implied the use of predictive algorithms. In addition, the measurement and acquisition of the output current and voltage of the PV panel, which increase circuit complexity, was avoided. Experimental measurements showed power consumptions of the MPPT controller as low as 52 μW for a 2.7-mW PV power and up to 388 μW for a 94.4-mW PV power. Tracking efficiency was higher than 99.4%. The overall efficiency was higher than 90% for a PV panel power higher than 20 mW. Field measurements showed an energy gain 15.7% higher than that of a direct-coupled solution.

Runtime Extension of Low-Power Wireless Sensor Nodes Using Hybrid-Storage Units

The sensor nodes of wireless sensor networks remain inactive most of the time to achieve longer runtimes. Power is mainly provided by batteries, which are either primary or secondary. Because of its internal impedance, a significant voltage drop can appear across the battery terminals at the activation time of the node, thus preventing the extraction of all the energy from the battery. Additionally, internal losses can also be significant. Consequently, the runtime is reduced. The addition of a supercapacitor in parallel with the battery, thus forming a hybrid-storage device, has been proposed under pulsed loads to increase the power capabilities and reduce both the voltage drop and the internal losses at the battery. However, this strategy has not yet thoroughly been analyzed and tested in low-power wireless sensor nodes. This paper presents a comprehensive theoretical analysis that extends previous works found in the literature and provides design guidelines for choosing the appropriate supercapacitor. The analysis is supported by extensive experimental results. Two low-capacity (< 200 mAh) batteries were tested together with their hybrid-storage unit counterparts when using an electronic load as a pulsed current sink. The hybrid-storage units always achieved a higher runtime. One of the batteries was also tested using a sensor node. The runtime extension was 16% and 33% when connecting the hybrid-storage unit directly and through a dc-dc switching regulator to the sensor node, respectively.

Powering wireless sensor nodes: Primary batteries versus energy harvesting

Wireless sensor networks (WSNs) are increasingly used in many fields. Still, power supply of the nodes remains a challenge. Primary batteries are mainly used but energy harvesting offers an alternative, although not free of problems. This paper compares the use of primary batteries against solar cells. Basic principles are first enunciated, then generic design examples are presented and finally actual deployed nodes of a WSN are illustrated.

Battery Squeezing under Low-Power Pulsed Loads

Autonomous sensors are wireless measurement systems that in order to achieve longer runtimes remain inactive most of the time and wake up only to acquire and send data. As a result, autonomous sensors can be modeled as low-power pulsed loads. Power is provided by batteries, either primary or secondary. Whenever the autonomous sensor wakes up, a significant voltage drop can appear between the battery terminals, thus preventing to squeeze all the available energy from the battery and leading to a reduction of the load runtime. This work proposes the use of hybrid-storage units, formed by the parallel combination of a battery and a (super)capacitor, in order to extend the runtime of low-power pulsed loads. We show analytical expressions of the transient voltage drop at the terminals of the hybrid-storage unit and deduce the minimal value of the capacitor to be used for a given voltage drop. Experimental results support the theoretical analysis. We tested several combinations of batteries and capacitors as the hybrid-storage unit under pulsed loads of tens of milliamps. Finally, we assessed the performance of the hybrid-storage unit when using as a load a commercial transceiver intended for wireless sensor networks applications.

REALnet: An environmental WSN Testbed

Wireless sensor networks (WSN) offer a powerful combination of distributed sensing, communication and computing. An environmental WSN testbed, REALnet is proposed and implemented at our University Campus. The objective is to monitor environmental parameters from the air, water and ground, and to become a generic platform that serves different purposes. This work presents the starting current state of REALnet. A temperature sensor and an original water level sensor for the Campus pond are implemented and a basic wireless network, composed of three nodes, is deployed to communicate the sensor node with a central station. The paper includes a brief description of all the parts that form the network from the sensors to the central node and the experimental achieved results.

Pervasive Computing Approaches to Environmental Sustainability

This issues Works in Progress department lists eight projects with a focus on environmental sustainability. The first three projects explore sensing and pervasive computing techniques for monitoring environmental conditions in outdoor situations. The next four projects use pervasive computing in indoor environments to inform individuals about their energy and resource consumption with the goal of positively influencing their behaviors. The final project aims to develop an energy generation infrastructure that combines multiple types of renewable energy sources.

A Simple and Efficient MPPT Method for Low-Power PV Cells

Small-size PV cells have been used to power sensor nodes. These devices present limited computing resources and so low complexity methods have been used in order to extract the maximum power from the PV cells. Among them, the fractional open circuit voltage (FOCV) method has been widely proposed, where the maximum power point of the PV cell is estimated from a fraction of its open circuit voltage. Here, we show a generalization of the FOCV method that keeps its inherent simplicity and improves the tracking efficiency. First, a single-diode model for PV cells was used to compute the tracking efficiency versus irradiance. Computations were carried out for different values of the parameters involved in the PV cell model. The proposed approach clearly outperformed the FOCV method, specially at low irradiance, which is significant for powering sensor nodes. Experimental tests performed with a 500 mW PV panel agreed with these results.