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Wearable devices with sensing, processing and communication capabilities have become feasible with the advances in internet-of-things (IoT) and low power design technologies. Energy harvesting is extremely important for wearable IoT devices due to size and weight limitations of batteries. One of the most widely used energy harvesting sources is photovoltaic cell (PV-cell) owing to its simplicity and high output power. In particular, flexible PV-cells offer great potential for wearable applications. This paper models, for the first time, how bending a PV-cell significantly impacts the harvested energy. Furthermore, we derive an analytical model to quantify the harvested energy as a function of the radius of curvature. We validate the proposed model empirically using a commercial PV-cell under a wide range of bending scenarios, light intensities and elevation angles. Finally, we show that the proposed model can accelerate maximum power point tracking algorithms and increase the harvested energy by up to 25.0%.
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How Much Energy Can We Harvest Daily for Wearable Applications?
Emerging flexible and stretchable devices open up novel and attractive applications beyond traditional rigid wearable devices. Since the small and flexible form-factor severely limits the battery capacity, energy harvesting (EH) stands out as a critical enabler of new devices. Despite increasing interest in recent years, the capacity of wearable energy harvesting remains unknown. Prior work analyzes the power generated by a single and typically rigid transducer. This choice limits the EH potential and undermines physical flexibility. Moreover, current results do not translate to total harvested energy over a given period, which is crucial from a developer perspective. In contrast, this paper explores the daily energy harvesting potential of combining flexible light and motion energy harvesters. It first presents a multi-modal energy harvesting system design whose inputs are flexible photo-voltaic cells and piezoelectric patches. We measure the generated power under various light intensity and gait speeds. Finally, we construct daily energy harvesting patterns of 9593 users by integrating our measurements with the activity data from the American Time Use Survey. Our results show that the proposed system can harvest on average 0. 6mAh @ 3. 6V per day.
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- Award ID(s):
- 2114499
- PAR ID:
- 10334239
- Date Published:
- Journal Name:
- IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED)
- Page Range / eLocation ID:
- 1 to 6
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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null (Ed.)As the number of Internet of Things (IoT) devices continues to increase, energy-harvesting (EH) devices eliminate the need to replace batteries or find outlets for sensors in indoor environments. This comes at a cost, however, as these energy-harvesting devices introduce new failure modes not present in traditional IoT devices: extended periods of no harvestable energy cause them to go dormant, their often simple wireless protocols are unreliable, and their limited energy reserves prohibit many diagnostic features. While energy-harvesting sensors promise easy-to-setup and maintenance-free deployments, their limitations hinder robust, long-term data collection. To continuously monitor and maintain a network of energy-harvesting devices in buildings, we propose the EH-HouseKeeper. EH-HouseKeeper is a data-driven system that monitors EH device compliance and predicts healthy signal zones in a building based on the existing gateway location(s) and building profile for easier device maintenance. EH-HouseKeeper does this by first filtering excess event-triggered data points and applying representation learning on building features that describe the path between the gateways and the device. We assessed EH-HouseKeeper by deploying 125 energy-harvesting sensors of varying types in a 17,000 square foot research infrastructure, randomly masking a quarter of the sensors as the test set for validation. The results of our 6-month data-collection period demonstrate an average greater than 80% accuracy in predicting the health status of the subset. Our results validate techniques for assessing sensor health status across device types, for inferring gateway status, and approaches to assist in identifying between gateway, transmission, and sensor faults.more » « less
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Recent advancements in wearable technology have improved lifestyle and medical practices, enabling personalized care ranging from fitness tracking, to real-time health monitoring, to predictive sensing. Wearable devices serve as an interface between humans and technology; however, this integration is far from seamless. These devices face various limitations such as size, biocompatibility, and battery constraints wherein batteries are bulky, are expensive, and require regular replacement. On-body energy harvesting presents a promising alternative to battery power by utilizing the human body’s continuous generation of energy. This review paper begins with an investigation of contemporary energy harvesting methods, with a deep focus on piezoelectricity. We then highlight the materials, configurations, and structures of such methods for self-powered devices. Here, we propose a novel combination of thin-film composites, kirigami patterns, and auxetic structures to lay the groundwork for an integrated piezoelectric system to monitor and sense. This approach has the potential to maximize energy output by amplifying the piezoelectric effect and manipulating the strain distribution. As a departure from bulky, rigid device design, we explore compositions and microfabrication processes for conformable energy harvesters. We conclude by discussing the limitations of these harvesters and future directions that expand upon current applications for wearable technology. Further exploration of materials, configurations, and structures introduce interdisciplinary applications for such integrated systems. Considering these factors can revolutionize the production and consumption of energy as wearable technology becomes increasingly prevalent in everyday life.more » « less
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Abstract: This paper aims to develop a novel concept for energy harvesting via flexible inverted flags combining photovoltaic cells with piezoelectric flexible films. Using technology currently available, we have designed and fabricated piezo-pyro-photo-electric harvesters made of polyvinylidene fluoride (PVDF) piezoelectric elements combined with mini solar panels made of silicon. Experimental measurements of the motion dynamics and power generation were collected for the flags when subjected to wind, heat, and light sources simultaneously and individually. Results indicate a significant improvement in energy harvesting capability compared to isolated single piezoelectric devices. Thus, we anticipate a substantial impact of piezo- pyro-photo-electric energy harvesting device applications where remote power generation is needed. The Flag uses flexible piezoelectric and pyroelectric strips and flexible photovoltaic cells panel. The piezo-pyro- simultaneously generates power through movement and heat, respectively, while the photovoltaic cells harvest solar energy to produce electric power. The beauty of this Flag is to develop power day and night depending on the energy sources available. The basic concept is presented and validated by laboratory experiments with controlled airflow, light, and infrared heat. The maximum voltage generated was 60 mV when the Flag was simultaneously exposed to low-level wind, thermal and light energies.more » « less
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As the next-generation battery substitute for IoT system, energy harvesting (EH) technology revolutionizes the IoT industry with environmental friendliness, ubiquitous accessibility, and sustainability, which enables various self-sustaining IoT applications. However, due to the weak and intermittent nature of EH power, the performance of EH-powered IoT systems as well as its collaborative routing mechanism can severely deteriorate, rendering unpleasant data package loss during each power failure. Such a phenomenon makes conventional routing policies and energy allocation strategies impractical. Given the complexity of the problem, reinforcement learning (RL) appears to be one of the most promising and applicable methods to address this challenge. Nevertheless, although the energy allocation and routing policy are jointly optimized by the RL method, due to the energy restriction of EH devices, the inappropriate configuration of multi-hop network topology severely degrades the data collection performance. Therefore, this article first conducts a thorough mathematical discussion and develops the topology design and validation algorithm under energy harvesting scenarios. Then, this article developsDeepIoTRouting, a distributed and scalable deep reinforcement learning (DRL)-based approach, to address the routing and energy allocation jointly for the energy harvesting powered distributed IoT system. The experimental results show that with topology optimization,DeepIoTRoutingachieves at least 38.71% improvement on the amount of data delivery to sink in a 20-device IoT network, which significantly outperforms state-of-the-art methods.more » « less
- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ISLPED52811.2021.9502507
