Energy-Efficient Wireless Connectivity and Wireless Charging For Internet-of-Things (IoT) Applications

During the recent years, the Internet-of-Things (IoT) has been rapidly evolving. It is indeed the future of communication that has transformed Things of the real world into smarter devices. To date, the world has deployed billions of “smart” connected things. Predictions say there will be 10’s of billions of connected devices by 2025 and in our lifetime we will experience life with a trillion-node network. However, battery lifespan exhibits a critical barrier to scaling IoT devices. Replacing batteries on a trillion-sensor scale is a logistically prohibitive feat. Self-powered IoT devices seems to be the right direction to stand up to that challenge. The main objective of this thesis is to develop solutions to achieve energy-efficient wireless-connectivity and wireless-charging for IoT applications. In the first part of the thesis, I introduce ultra-low power radios that are compatible with the Bluetooth Low-Energy (BLE) standard. BLE is considered as the preeminent protocol for short-range communications that support transmission ranges up to 10’s of meters. Number of low power BLE transmitter (TX) and receiver (RX) architectures have been designed, fabricated and tested in different planar CMOS and FinFET technologies. The low power operation is achieved by combining low power techniques in both the network and physical layers, namely: backchannel communication, duty-cycling, open-loop transmission/reception, PLL-less architectures, and mixer-first architectures. Further novel techniques have been proposed to further reduce the power the consumption of the radio design, including: a fast startup time and low startup energy crystal oscillators, an antenna-chip co-design approach for quadrature generation in the RF path, an ultra-low power discrete-time differentiator-based Gaussian Frequency Shift Keying (GFSK) demodulation scheme, an oversampling GFSK modulation/demodulation scheme for open loop transmission/reception and packet synchronization, and a cell-based design approach that allows automation in the design of BLE digital architectures. The implemented BLE TXs transmit fully-compliant BLE advertising packet that can be received by commercial smartphone. In the second part of the thesis, I introduce passive nonlinear resonant circuits to achieve wide-band RF energy harvesting and robust wireless power transfer circuits. Nonlinear resonant circuits modeled by the Duffing nonlinear differential equation exhibit interesting hysteresis characteristics in their frequency and amplitude responses that are exploited in designing self-adaptive wireless charging systems. In the magnetic-resonance wireless power transfer scenario, coupled nonlinear resonators are proposed to maintain the power transfer level and efficiency over a range of coupling factors without active feedback control circuitry. Coupling factor depends on the transmission distance, lateral, and angular misalignments between the charging pad and the device. Therefore, nonlinear resonance extends the efficient charging zones of a wireless charger without the requirement for a precise alignment.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/169842/1/omaratty_1.pd

Millimeter-Scale and Energy-Efficient RF Wireless System

This dissertation focuses on energy-efficient RF wireless system with millimeter-scale dimension, expanding the potential use cases of millimeter-scale computing devices. It is challenging to develop RF wireless system in such constrained space. First, millimeter-sized antennae are electrically-small, resulting in low antenna efficiency. Second, their energy source is very limited due to the small battery and/or energy harvester. Third, it is required to eliminate most or all off-chip devices to further reduce system dimension. In this dissertation, these challenges are explored and analyzed, and new methods are proposed to solve them. Three prototype RF systems were implemented for demonstration and verification. The first prototype is a 10 cubic-mm inductive-coupled radio system that can be implanted through a syringe, aimed at healthcare applications with constrained space. The second prototype is a 3x3x3 mm far-field 915MHz radio system with 20-meter NLOS range in indoor environment. The third prototype is a low-power BLE transmitter using 3.5x3.5 mm planar loop antenna, enabling millimeter-scale sensors to connect with ubiquitous IoT BLE-compliant devices. The work presented in this dissertation improves use cases of millimeter-scale computers by presenting new methods for improving energy efficiency of wireless radio system with extremely small dimensions. The impact is significant in the age of IoT when everything will be connected in daily life.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147686/1/yaoshi_1.pd

An Input Power-Aware Maximum Efficiency Tracking Technique for Energy Harvesting in IoT Applications

The Internet of Things (IoT) enables intelligent monitoring and management in many applications such as industrial and biomedical systems as well as environmental and infrastructure monitoring. As a result, IoT requires billions of wireless sensor network (WSN) nodes equipped with a microcontroller and transceiver. As many of these WSN nodes are off-grid and small-sized, their limited-capacity batteries need periodic replacement. To mitigate the high costs and challenges of these battery replacements, energy harvesting from ambient sources is vital to achieve energy-autonomous operation. Energy harvesting for WSNs is challenging because the available energy varies significantly with ambient conditions and in many applications, energy must be harvested from ultra-low power levels. To tackle these stringent power constraints, this dissertation proposes a discontinuous charging technique for switched-capacitor converters that improves the power conversion efficiency (PCE) at low input power levels and extends the input power harvesting range at which high PCE is achievable. Discontinuous charging delivers current to energy storage only during clock non-overlap time. This enables tuning of the output current to minimize converter losses based on the available input power. Based on this fundamental result, an input power-aware, two-dimensional efficiency tracking technique for WSNs is presented. In addition to conventional switching frequency control, clock nonoverlap time control is introduced to adaptively optimize the power conversion efficiency according to the sensed ambient power levels. The proposed technique is designed and simulated in 90nm CMOS with post-layout extraction. Under the same input and output conditions, the proposed system maintains at least 45% PCE at 4μW input power, as opposed to a conventional continuous system which requires at least 18.7μW to maintain the same PCE. In this technique, the input power harvesting range is extended by 1.5x. The technique is applied to a WSN implementation utilizing the IEEE 802.15.4- compatible GreenNet communications protocol for industrial and wearable applications. This allows the node to meet specifications and achieve energy autonomy when deployed in harsher environments where the input power is 49% lower than what is required for conventional operation

Analysis and Design of Energy Efficient Frequency Synthesizers for Wireless Integrated Systems

Advances in ultra-low power (ULP) circuit technologies are expanding the IoT applications in our daily life. However, wireless connectivity, small form factor and long lifetime are still the key constraints for many envisioned wearable, implantable and maintenance-free monitoring systems to be practically deployed at a large scale. The frequency synthesizer is one of the most power hungry and complicated blocks that not only constraints RF performance but also offers subtle scalability with power as well. Furthermore, the only indispensable off-chip component, the crystal oscillator, is also associated with the frequency synthesizer as a reference. This thesis addresses the above issues by analyzing how phase noise of the LO affect the frequency modulated wireless system in different aspects and how different noise sources in the PLL affect the performance. Several chip prototypes have been demonstrated including: 1) An ULP FSK transmitter with SAR assisted FLL; 2) A ring oscillator based all-digital BLE transmitter utilizing a quarter RF frequency LO and 4X frequency multiplier; and 3) An XO-less BLE transmitter with an RF reference recovery receiver. The first 2 designs deal with noise sources in the PLL loop for ultimate power and cost reduction, while the third design deals with the reference noise outside the PLL and explores a way to replace the XO in ULP wireless edge nodes. And at last, a comprehensive PN theory is proposed as the design guideline.PHDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153420/1/chenxing_1.pd

Spatio-temporal prediction os electric power systems including emergent renewable energy sources

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Elétrica, 2014.A atividade de planejamento de sistemas de potência inclui, como um de seus maiores desafios, a predicação do comportamento da carga. Com a finalidade de otimizar oinvestimento ante os dados de consumo, as empresas do setor elétrico lançam mão de várias técnicas de previsão da evolução da demanda que devem atender. No presente trabalho, o tema da predição espacial e temporal da carga é enfrentado, estudando e incorporando, simultaneamente, a tendência hoje já observada de inclusão de fonte sem microgeração distribuída. Três fontes renováveis e emergentes de geração foram consideradas como geradoras de energia pelos consumidores: enguias elétricas, painéis fotovoltaicos para aproveitamento da luz solar e de interiores, e antenas para reciclagemda energia existente nas ondas eletromagnéticas de radiodifusão. Quatro métodos preditivos foram empregados para prever o comportamento da carga: modelo Auto-Regressivo (AR), Auto-Regressivo com Variável eXógena (ARX), Auto-Regressivo deMédia Móvel com Variável eXógena (ARMAX) e Redes Neurais Artificiais (ANN). Os dados de consumo foram as máximas demandas semanais registradas em 8 Subestações da cidade de Leipzig (Saxônia, Alemanha), durante os anos de 2001, 2002, 2003 e 2004.O dado exôgeno considerado foi a temperatura, em valores diretos e logarítmicos. Das 209 semanas existentes entre 2001 e 2004, as 200 primeiras destinaram-se ao ajuste dos coeficientes nos modelos AR e ao treinamento da rede neural; as 9 semanas restantesforam destinadas à comparação de resultados. A aplicação das técnicas deu-se, assim,em dois estágios: no primeiro, os dados reais da rede de Leipzig foram considerados, eno segundo estágio trabalhou-se com novos valores de demandas máximas, originadaspela inserção de valores hipotéticos de energia recebida das três fontes citadas. Emambos os estágios, o modelo ARMAX foi o de melhor precisão na previsão de dados.O sistema de redes neurais demonstrou ser um sistema sub-ótimo de previsão. ______________________________________________________________________________ ABSTRACTPower systems planning activities include load behavior prediction as one of its mostchallenging tasks. In order to optmize investments related to consumption data, utilitiesfrom the Electrical Sector resort to several forecasting techniques so that theycan predict the power demand which these utilities must support. Along the presentwork, issues related to the spatial and temporal predictions are faced, considering,simultaneously, the observed trend of microgeneration spread. Three emergent renewablesources were proposed to be taken on by consumers: electric eels, photovoltaicsolar panels for outdoor generation and indoor light energy harvesting, and antennasfor radio frequency energy recycling. Four predictive methods were employed in orderto forecast load evolution: Auto-Regressive (AR), Auto-Regressive with eXogeneousinputs (ARX), Auto-Regressive Moving Average with eXogeneous inputs (ARMAX)models and Artificial Neural Networks (ANN). Consumption data were the maximumweekly power demands registered over 8 Power Substations from the city of Leipzig(Saxony, Germany), during the years 2001, 2002, 2003 and 2004. The exogeneousvariable adopted was temperature, in realistic and in logarithmic values. During the209 weeks which are comprised between 2001 and 2004, the _rst 200 weeks served tocoe_cients adjustments, with regards to AR models, and the trainning of the neuralnetwork, in the case of ANN. The last 9 weeks were destinated for results comparison.Techniques were undertaken in two stages: _rstly, only realistic data from LeipzigSubstations were considered, and in the second stage, new values for maximum powerdemands were obtained by means of simulations upon the three emergent sources. Inboth stages, ARMAX model returned the _ttest results, whereas ANN characterizeditself as a sub-optimal prediction system