Indoor 2.45 GHz Wi-Fi Energy Harvester With Bridgeless Converter

Ermeey, A. K.; Hu, Aiguo Patrick; Biglari-Abhari, Morteza; Aw, Kean C.

doi:10.1109/jsac.2016.2551578

Cited by 8 publications

(9 citation statements)

References 17 publications

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“…Similarly to motion harvesters, PV power generation is highly situation specific and completely fails in many usage scenarios where insufficient light conditions are present. RF on the other hand is independent from light and movement but requires power lines or machinery in the direct vicinity and only provides a few nanowatts of harvested power [21,8].…”

Section: Related Workmentioning

confidence: 99%

Human body heat for powering wearable devices: From thermal energy to application

Thielen

Sigrist

Magno

et al. 2017

Energy Conversion and Management

201

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Section: Related Workmentioning

confidence: 99%

Human body heat for powering wearable devices: From thermal energy to application

Thielen

Sigrist

Magno

et al. 2017

Energy Conversion and Management

201

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“…Later, we will show that the optimal matrix W * n satisfies rank (W * n ) ≤ 1 for each n ∈ N . For properties of the optimal solution to problem (8), we study the following rate maximization problem with given transmission power p l,n for each BS l and RF charging constraint q n for the energy receiver at slot n:…”

Section: B Achievable Energy-throughput Regionmentioning

confidence: 99%

“…Particularly, radio-frequency (RF) energy of wireless signals can also be exploited to charge lowpower devices remotely, which is referred to as RF charging. For instance, sufficient energy can be collected by ambient signals from TV towers [7] or Wi-Fi networks [8], and dedicated energy signals have been considered to charge devices in the wireless powered communication network (WPCN) [9]. Moreover, since wireless signals can carry information and energy at the same time, it is possible to perform simultaneous wireless information and energy transfer (SWIPT) [10] for either co-located information and energy receivers or separated located receivers.…”

Section: Introductionmentioning

confidence: 99%

Energy-throughput tradeoff in sustainable Cloud-RAN with energy harvesting

Chen

Cai

et al. 2017

2017 IEEE International Conference on Communications (ICC)

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Abstract-In this paper, we investigate joint beamforming for energy-throughput tradeoff in a sustainable cloud radio access network system, where multiple base stations (BSs) powered by independent renewable energy sources will collaboratively transmit wireless information and energy to the data receiver and the energy receiver simultaneously. In order to obtain the optimal joint beamforming design over a finite time horizon, we formulate an optimization problem to maximize the throughput of the data receiver while guaranteeing sufficient RF charged energy of the energy receiver. Although such problem is non-convex, it can be relaxed into a convex form and upper bounded by the optimal value of the relaxed problem. We further prove tightness of the upper bound by showing the optimal solution to the relaxed problem is rank one. Motivated by the optimal solution, an efficient online algorithm is also proposed for practical implementation. Finally, extensive simulations are performed to verify the superiority of the proposed joint beamforming strategy to other beamforming designs.Index Terms-Energy harvesting, wireless information and power transfer, beamforming, Cloud-RAN.

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“…Radio Frequency (RF) EH devices are able to harvest the RF energy transmitted by mobile communication device, WiFi and Base Station to power low-power wearable devices. The indoor 2.45 GHz WiFi energy harvester designed by Ermeey et al outputs 1.62 mW of electrical energy for driving external low-power devices [18]. Presently, the RF energy harvester is facing the urgent problems of antenna, sensitivity, and conversion efficiency.…”

Section: Introductionmentioning

confidence: 99%

A New Portable Energy Harvesting Device Mounted on Shoes: Performance and Impact on Wearer

Wang

Liu

et al. 2020

Energies

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With the rapid development of microelectronic technology, increasing wearable devices are available for us in daily lives. These wearable devices are usually powered by batteries, so the endurance of wearable devices is limited by battery capacity. Biomechanical energy harvesting (EH) is a promising approach to extend the endurance or replace batteries to power microelectronic devices. In this paper, we would first review the different types of EH devices and analyze the key technologies. Secondly, to compare the advantages and disadvantages of different EH devices, the design guidelines of the EH device is proposed. According to the design guidelines, an EH device with a weight of only 110 g is designed, which is lighter than the suspended-load backpack (38 kg) and the knee harvester (1.6 kg). Our EH device is able to convert the vibrations energy generated by the motion of lower limbs into electrical energy. Finally, to verify whether our EH device meets the design guidelines, three sets of experiment are conducted to evaluate the performance of the EH device. Experiment A is used to monitor the metabolic energy and electricity energy. According to experiment data, we calculate the COH and TCOH of EH device. The lowest COH and TCOH are 58 and 59, respectively. The TCOH of our EH device is higher than suspended-load backpack (30.7) and the knee harvester (13.6). Experiment B is used to test the adaptability of the EH device under the different conditions of terrains and walking speeds. The EH device is able to harvest energy in different terrains (up and down stair, at the treadmill with a slope of 0°, 5° and 10°) and at different speeds(4 km/h~8 km/h). The converted electrical power is up to 7.71 mW during walking at the speed of 8 km/h and is up to 5.28 mW when going up stairs. Experiment C is conducted to investigate the influence of the EH device on human motion by measuring the torque of the ankle, knee, and hip joints. When comparing the curves of torque, the EH device does not influence the biological moment of joints, indicating the EH device does not interfere with the movement. At the same time, another method to calculate the TCOH by calculating the net work done by the muscle in a gait cycle is proposed. When comparing the TCOH calculated by the metabolic energy with the TCOH calculated by the additional work done by the muscle, the results are basically the same. Meanwhile, the variation of the latter calculation result is smaller, indicating the method proposed by us is more precise.

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Indoor 2.45 GHz Wi-Fi Energy Harvester With Bridgeless Converter

Cited by 8 publications

References 17 publications

Human body heat for powering wearable devices: From thermal energy to application

Human body heat for powering wearable devices: From thermal energy to application

Energy-throughput tradeoff in sustainable Cloud-RAN with energy harvesting

A New Portable Energy Harvesting Device Mounted on Shoes: Performance and Impact on Wearer

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