Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Power Beacon’s deployment optimization for wirelessly powering massive Internet of Things networks

Abstract. The fifth-generation (5G) and beyond wireless cellular networks promise the native support to, among other use cases, the so-called Internet of Things (IoT). Different from human-based cellular services, IoT networks implement a novel vision where ordinary machines possess the ability to autonomously sense, actuate, compute, and communicate throughout the Internet. However, as the number of connected devices grows larger, an urgent demand for energy-efficient communication technologies arises. A key challenge related to IoT devices is that their very small form factor allows them to carry just a tiny battery that might not be even possible to replace due to installation conditions, or too costly in terms of maintenance because of the massiveness of the network. This issue limits the lifetime of the network and compromises its reliability. Wireless energy transfer (WET) has emerged as a potential candidate to replenish sensors’ batteries or to sustain the operation of battery-free devices, as it provides a controllable source of energy over-the-air. Therefore, WET eliminates the need for regular maintenance, allows sensors’ form factor reduction, and reduces the battery disposal that contributes to the environment pollution. In this thesis, we review some WET-enabled scenarios and state-of-the-art techniques for implementing WET in IoT networks. In particular, we focus our attention on the deployment optimization of the so-called power beacons (PBs), which are the energy transmitters for charging a massive IoT deployment subject to a network-wide probabilistic energy outage constraint. We assume that IoT sensors’ positions are unknown at the PBs, and hence we maximize the average incident power on the worst network location. We propose a linear-time complexity algorithm for optimizing the PBs’ positions that outperforms benchmark methods in terms of minimum average incident power and computation time. Then, we also present some insights on the maximum coverage area under certain propagation conditions

Datacenter management for on-site intermittent and uncertain renewable energy sources

Les technologies de l'information et de la communication sont devenues, au cours des dernières années, un pôle majeur de consommation énergétique avec les conséquences environnementales associées. Dans le même temps, l'émergence du Cloud computing et des grandes plateformes en ligne a causé une augmentation en taille et en nombre des centres de données. Pour réduire leur impact écologique, alimenter ces centres avec des sources d'énergies renouvelables (EnR) apparaît comme une piste de solution. Cependant, certaines EnR telles que les énergies solaires et éoliennes sont liées aux conditions météorologiques, et sont par conséquent intermittentes et incertaines. L'utilisation de batteries ou d'autres dispositifs de stockage est souvent envisagée pour compenser ces variabilités de production. De par leur coût important, économique comme écologique, ainsi que les pertes énergétiques engendrées, l'utilisation de ces dispositifs sans intégration supplémentaire est insuffisante. La consommation électrique d'un centre de données dépend principalement de l'utilisation des ressources de calcul et de communication, qui est déterminée par la charge de travail et les algorithmes d'ordonnancement utilisés. Pour utiliser les EnR efficacement tout en préservant la qualité de service du centre, une gestion coordonnée des ressources informatiques, des sources électriques et du stockage est nécessaire. Il existe une grande diversité de centres de données, ayant différents types de matériel, de charge de travail et d'utilisation. De la même manière, suivant les EnR, les technologies de stockage et les objectifs en termes économiques ou environnementaux, chaque infrastructure électrique est modélisée et gérée différemment des autres. Des travaux existants proposent des méthodes de gestion d'EnR pour des couples bien spécifiques de modèles électriques et informatiques. Cependant, les multiples combinaisons de ces deux parties rendent difficile l'extrapolation de ces approches et de leurs résultats à des infrastructures différentes. Cette thèse explore de nouvelles méthodes pour résoudre ce problème de coordination. Une première contribution reprend un problème d'ordonnancement de tâches en introduisant une abstraction des sources électriques. Un algorithme d'ordonnancement est proposé, prenant les préférences des sources en compte, tout en étant conçu pour être indépendant de leur nature et des objectifs de l'infrastructure électrique. Une seconde contribution étudie le problème de planification de l'énergie d'une manière totalement agnostique des infrastructures considérées. Les ressources informatiques et la gestion de la charge de travail sont encapsulées dans une boîte noire implémentant un ordonnancement sous contrainte de puissance. La même chose s'applique pour le système de gestion des EnR et du stockage, qui agit comme un algorithme d'optimisation d'engagement de sources pour répondre à une demande. Une optimisation coopérative et multiobjectif, basée sur un algorithme évolutionnaire, utilise ces deux boîtes noires afin de trouver les meilleurs compromis entre les objectifs électriques et informatiques. Enfin, une troisième contribution vise les incertitudes de production des EnR pour une infrastructure plus spécifique. En utilisant une formulation en processus de décision markovien (MDP), la structure du problème de décision sous-jacent est étudiée. Pour plusieurs variantes du problème, des méthodes sont proposées afin de trouver les politiques optimales ou des approximations de celles-ci avec une complexité raisonnable.In recent years, information and communication technologies (ICT) became a major energy consumer, with the associated harmful ecological consequences. Indeed, the emergence of Cloud computing and massive Internet companies increased the importance and number of datacenters around the world. In order to mitigate economical and ecological cost, powering datacenters with renewable energy sources (RES) began to appear as a sustainable solution. Some of the commonly used RES, such as solar and wind energies, directly depends on weather conditions. Hence they are both intermittent and partly uncertain. Batteries or other energy storage devices (ESD) are often considered to relieve these issues, but they result in additional energy losses and are too costly to be used alone without more integration. The power consumption of a datacenter is closely tied to the computing resource usage, which in turn depends on its workload and on the algorithms that schedule it. To use RES as efficiently as possible while preserving the quality of service of a datacenter, a coordinated management of computing resources, electrical sources and storage is required. A wide variety of datacenters exists, each with different hardware, workload and purpose. Similarly, each electrical infrastructure is modeled and managed uniquely, depending on the kind of RES used, ESD technologies and operating objectives (cost or environmental impact). Some existing works successfully address this problem by considering a specific couple of electrical and computing models. However, because of this combined diversity, the existing approaches cannot be extrapolated to other infrastructures. This thesis explores novel ways to deal with this coordination problem. A first contribution revisits batch tasks scheduling problem by introducing an abstraction of the power sources. A scheduling algorithm is proposed, taking preferences of electrical sources into account, though designed to be independent from the type of sources and from the goal of the electrical infrastructure (cost, environmental impact, or a mix of both). A second contribution addresses the joint power planning coordination problem in a totally infrastructure-agnostic way. The datacenter computing resources and workload management is considered as a black-box implementing a scheduling under variable power constraint algorithm. The same goes for the electrical sources and storage management system, which acts as a source commitment optimization algorithm. A cooperative multiobjective power planning optimization, based on a multi-objective evolutionary algorithm (MOEA), dialogues with the two black-boxes to find the best trade-offs between electrical and computing internal objectives. Finally, a third contribution focuses on RES production uncertainties in a more specific infrastructure. Based on a Markov Decision Process (MDP) formulation, the structure of the underlying decision problem is studied. For several variants of the problem, tractable methods are proposed to find optimal policies or a bounded approximation

Duty-cycled Wake-up Schemes for Ultra-low Power Wireless Communications

In sensor network applications with low traffic intensity, idle channel listening is one of the main sources of energy waste.The use of a dedicated low-power wake-up receiver (WRx) which utilizes duty-cycled channel listening can significantlyreduce idle listening energy cost. In this thesis such a scheme is introduced and it is called DCW-MAC, an acronym forduty-cycled wake-up receiver based medium access control.We develop the concept in several steps, starting with an investigation into the properties of these schemes under idealizedconditions. This analysis show that DCW-MAC has the potential to significantly reduce energy costs, compared to twoestablished reference schemes based only on low-power wake up receivers or duty-cycled listening. Findings motivatefurther investigations and more detailed analysis of energy consumption. We do this in two separate steps, first concentratingon the energy required to transmit wake-up beacons and later include all energy costs in the analysis. The more completeanalysis makes it possible to optimize wake-up beacons and other DCW-MAC parameters, such as sleep and listen intervals,for minimal energy consumption. This shows how characteristics of the wake-up receiver influence how much, and if, energycan be saved and what the resulting average communication delays are. Being an analysis based on closed form expressions,rather than simulations, we can derive and verify good approximations of optimal energy consumption and resulting averagedelays, making it possible to quickly evaluate how a different wake-up receiver characteristic influences what is possible toachieve in different scenarios.In addition to the direct optimizations of the DCW-MAC scheme, we also provide a proof-of-concept in 65 nm CMOS,showing that the digital base-band needed to implement DCW-MAC has negligible energy consumption compared to manylow-power analog front-ends in literature. We also propose a a simple frame-work for comparing the relative merits ofanalog front-ends for wake-up receivers, where we use the experiences gained about DCW-MAC energy consumption toprovide a simple relation between wake-up receiver/analog front-end properties and energy consumption for wide ranges ofscenario parameters. Using this tool it is possible to compare analog front-ends used in duty-cycled wake-up schemes, evenif they are originally designed for different scenarios.In all, the thesis presents a new wake-up receiver scheme for low-power wireless sensor networks and provide a comprehensiveanalysis of many of its important properties

Worst-case energy consumption: A new challenge for battery-powered critical devices

The number of devices connected to the IoT is on the rise, reaching hundreds of billions in the next years. Many devices will implement some type of critical functionality, for instance in the medical market. Energy awareness is mandatory in the design of IoT devices because of their huge impact on worldwide energy consumption and the fact that many of them are battery powered. Critical IoT devices further require addressing new energy-related challenges. On the one hand, factoring in the impact of energy-solutions on device's performance, providing evidence of adherence to domain-specific safety standards. On the other hand, deriving safe worst-case energy consumption (WCEC) estimates is a fundamental building block to ensure the system can continuously operate under a pre-established set of power/energy caps, safely delivering its critical functionality. We analyze for the first time the impact that different hardware physical parameters have on both model-based and measurement-based WCEC modeling, for which we also show the main challenges they face compared to chip manufacturers' current practice for energy modeling and validation. Under the set of constraints that emanate from how certain physical parameters can be actually modeled, we show that measurement-based WCEC is a promising way forward for WCEC estimation.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (MINECO) under grant TIN2015- 65316-P and the HiPEAC Network of Excellence. Jaume Abella has been partially supported by the MINECO under Ramon y Cajal postdoctoral fellowship number RYC-2013-14717. Carles Hernndez is jointly funded by the MINECO and FEDER funds through grant TIN2014-60404-JIN.Peer ReviewedPostprint (author's final draft

Real-time scheduling for energy harvesting sensor nodes

Energy harvesting has recently emerged as a feasible option to increase the operating time of sensor networks. If each node of the network, however, is powered by a fluctuating energy source, common power management solutions have to be reconceived. This holds in particular if real-time responsiveness of a given application has to be guaranteed. Task scheduling at the single nodes should account for the properties of the energy source, capacity of the energy storage as well as deadlines of the single tasks. We show that conventional scheduling algorithms (like e.g. EDF) are not suitable for this scenario. Based on this motivation, we have constructed optimal scheduling algorithms that jointly handle constraints from both energy and time domain. Further we present an admittance test that decides for arbitrary task sets, whether they can be scheduled without deadline violations. To this end, we introduce the concept of energy variability characterization curves (EVCC) which nicely captures the dynamics of various energy sources. Simulation results show that our algorithms allow significant reductions of the battery size compared to Earliest Deadline First schedulin

The Interplay of Reward and Energy in Real-Time Systems

This work contends that three constraints need to be addressed in the context of power-aware real-time systems: energy, time and task rewards/values. These issues are studied for two types of systems. First, embedded systems running applications that will include temporal requirements (e.g., audio and video). Second, servers and server clusters that have timing constraints and Quality of Service (QoS) requirements implied by the application being executed (e.g., signal processing, audio/video streams, webpages). Furthermore, many future real-time systems will rely on different software versions to achieve a variety of QoS-aware tradeoffs, each with different rewards, time and energy requirements.For hard real-time systems, solutions are proposed that maximize the system reward/profit without exceeding the deadlines and without depleting the energy budget (in portable systems the energy budget is determined by the battery charge, while in server farms it is dependent on the server architecture and heat/cooling constraints). Both continuous and discrete reward and power models are studied, and the reward/energy analysis is extended with multiple task versions, optional/mandatory tasks and long-term reward maximization policies.For soft real-time systems, the reward model is relaxed into a QoS constraint, and stochastic schemes are first presented for power management of systems with unpredictable workloads. Then, load distribution and power management policies are addressed in the context of servers and homogeneous server farms. Finally, the work is extended with QoS-aware local and global policies for the general case of heterogeneous systems

LIBRA: An inexpensive geodetic network densification system

A description is given of the Libra (Locations Interposed by Ranging Aircraft) system, by which geodesy and earth strain measurements can be performed rapidly and inexpensively to several hundred auxiliary points with respect to a few fundamental control points established by any other technique, such as radio interferometry or satellite ranging. This low-cost means of extending the accuracy of space age geodesy to local surveys provides speed and spatial resolution useful, for example, for earthquake hazards estimation. Libra may be combined with an existing system, Aries (Astronomical Radio Interferometric Earth Surveying) to provide a balanced system adequate to meet the geophysical needs, and applicable to conventional surveying. The basic hardware design was outlined and specifications were defined. Then need for network densification was described. The following activities required to implement the proposed Libra system are also described: hardware development, data reduction, tropospheric calibrations, schedule of development and estimated costs

Energy efficiency in LoRaWAN

Abstract. Low-power wide-area networks (LPWANs) are emerging rapidly as a fundamental Internet of Things (IoT) technology because of features like low-power consumption, long-range connectivity, and the ability to support massive numbers of users. With its high growth rate, Long Range (LoRa) is becoming the most adopted LPWAN technology. Sensor nodes are typically powered by batteries, and many network applications, which expect end-devices to operate reliably for a prolonged time. Each sensor node or actuator consumes a distinct current for a different period of time, depending on its operational state. To model a self-sufficient sensor nodes network, it is of the utmost importance to investigate the energy consumption of class-A end-devices in a LoRa Wide Area Network (LoRaWAN) with the impact of respective physical and MAC layers. Several latest published research works have analyzed the energy consumption model of a sensor node in different transmission (confirmed or unconfirmed) modes and also examined the network performance of LoRaWAN under uplink outage probabilities. This research work investigates the energy cost of the LoRaWAN, deploying hundreds of sensor nodes to transmit information messages. The proposed scheme is evaluated by considering the average power consumption of end-device powered by 2400 mAh battery. Furthermore, the energy efficiency of an unconfirmed transmission network is examined to provide the optimal number of sensor nodes for each spreading factor