Charging Requests Research Articles

Maximizing Charging Satisfaction of Smartphone Users via Wireless Energy Transfer

Smartphones now become an indispensable part of our daily life. However, maintaining a smartphone's continuing operation consumes lots of battery energy. For example, a fully-charged smartphone usually cannot support its continuing operation for a whole day. A fundamental issue on a smartphone is its energy issue. That is, how to prolong the lifetime of a smartphone so that it can run as long as possible to meet its user needs. Wireless energy transfer has been demonstrated as a promising technique to address this issue. In this paper, we study a novel smartphone charging problem, through wireless chargers deployed on public commuters, e.g., subway trains, to charge energy-critical smartphones when their users take subway trains to work or go home. Since the amounts of residual energy of different smartphones are significantly different, the charging satisfactions of different users are essentially different. In this paper, we formulate this charging satisfaction problem as a novel optimization problem that schedules the limited number of wireless chargers on subway trains to charge energy-critical smartphones such that the overall charging satisfaction of smartphone users is maximized, for a given monitoring period (e.g., one day). Forthis problem, we first devise a 1/3-approximation algorithm if the travel trajectory of each smartphone user is given. We then propose an online algorithm to deal with dynamic energy-critical smartphone charging requests. We also propose a nontrivial distributed scheduling algorithm for a variant of the problem where the global knowledge of user energy information is unknown. We finally evaluate the performance of the proposed algorithms through experimental simulations, using a real dataset of subway-taking in San Francisco. The experimental results show that the proposed algorithms are very promising, and over 90 percent of energy-critical user smartphones can be satisfactorily charged in a one-day monitoring period.

Distributed Charge Scheduling of Plug-In Electric Vehicles Using Inter-Aggregator Collaboration

Plug-in electric vehicles (PEVs) are emerging as an eco-friendly and cost-effective alternative to conventional vehicles driven by internal combustion engines. However, uncoordinated charging of a large number of PEVs may cause grid failure. Therefore, charge scheduling of PEVs is an important problem. However, the charge scheduling by a single aggregator does not scale well as the PEV population grows. We propose a distributed framework for efficient PEV charging with multiple aggregators in a city where a PEV raises its charging request to a specific aggregator, and each aggregator has partial information about others. The aggregators collaborate among themselves for scheduling PEVs for charging in different charging stations owned by it or others. In this paper, we formulate a bi-objective charge scheduling optimization problem that attempts to maximize the total profit of the aggregators while maximizing the total number of PEVs charged. We first prove that the problem is NP-complete. We then propose distributed offline and online algorithms to solve the problem, and present simulation results for some realistic traffic scenarios.

Threshold-Based Random Charging Scheme for Decentralized PEV Charging Operation in a Smart Grid.

Smart grids have been introduced to replace conventional power distribution systems without real time monitoring for accommodating the future market penetration of plug-in electric vehicles (PEVs). When a large number of PEVs require simultaneous battery charging, charging coordination techniques have become one of the most critical factors to optimize the PEV charging performance and the conventional distribution system. In this case, considerable computational complexity of a central controller and exchange of real time information among PEVs may occur. To alleviate these problems, a novel threshold-based random charging (TBRC) operation for a decentralized charging system is proposed. Using PEV charging thresholds and random access rates, the PEVs themselves can participate in the charging requests. As PEVs with a high battery state do not transmit the charging requests to the central controller, the complexity of the central controller decreases due to the reduction of the charging requests. In addition, both the charging threshold and the random access rate are statistically calculated based on the average of supply power of the PEV charging system that do not require a real time update. By using the proposed TBRC with a tolerable PEV charging degradation, a 51% reduction of the PEV charging requests is achieved.

Distributed Control for Charging Multiple Electric Vehicles with Overload Limitation

Severe pollution induced by traditional fossil fuels arouses great attention on the usage of plug-in electric vehicles (PEVs) and renewable energy. However, large-scale penetration of PEVs combined with other kinds of appliances tends to cause excessive or even disastrous burden on the power grid, especially during peak hours. This paper focuses on the scheduling of PEVs charging process among different charging stations and each station can be supplied by both renewable energy generators and a distribution network. The distribution network also powers some uncontrollable loads. In order to minimize the on-grid energy cost with local renewable energy and non-ideal storage while avoiding the overload risk of the distribution network, an online algorithm consisting of scheduling the charging of PEVs and energy management of charging stations is developed based on Lyapunov optimization and Lagrange dual decomposition techniques. The algorithm can satisfy the random charging requests from PEVs with provable performance. Simulation results with real data demonstrate that the proposed algorithm can decrease the time-average cost of stations while avoiding overload in the distribution network in the presence of random uncontrollable loads.

Computational resource management for data‐driven applications with deadline constraints

SummaryRecent advances in the type and variety of sensing technologies have led to an extraordinary growth in the volume of data being produced and led to a number of streaming applications that make use of this data. Sensors typically monitor environmental or physical phenomenon at predefined time intervals or triggered by user‐defined events. Understanding how such streaming content (the raw data or events) can be processed within a time threshold remains an important research challenge. We investigate how a cloud‐based computational infrastructure can autonomically respond to such streaming content, offering quality of service guarantees. In particular, we contextualize our approach using an electric vehicles (EVs) charging scenario, where such vehicles need to connect to the electrical grid to charge their batteries. There has been an emerging interest in EV aggregators (primarily intermediate brokers able to estimate aggregate charging demand for a collection of EVs) to coordinate the charging process. We consider predicting EV charging demand as a potential workload with execution time constraints. We assume that an EV aggregator manages a number of geographic areas and a pool of computational resources of a cloud computing cluster to support scheduling of EV charging. The objective is to ensure that there is enough computational capacity to satisfy the requirements for managing EV battery charging requests within specific time constraints.

Efficient on-demand multi-node charging techniques for wireless sensor networks

This paper deals with wireless charging in sensor networks and explores efficient policies to perform simultaneous multi-node power transfer through a mobile charger (MC).The proposed solution, called On-demand Multi-node Charging (OMC), features an original threshold-based tour launching (TTL) strategy, using request grouping, and a path planning algorithm based on minimizing the number of stopping points in the charging tour. Contrary to existing solutions, which focus on shortening the charging delays, OMC groups incoming charging requests and optimizes the charging tour and the mobile charger energy consumption. Although slightly increasing the waiting time before nodes are charged, this allows taking advantage of multiple simultaneous charges and also reduces node failures. At the tour planning level, a new modeling approach is used. It leverages simultaneous energy transfer to multiple nodes by maximizing the number of sensors that are charged at each stop. Given its NP-hardness, tour planning is approximated through a clique partitioning problem, which is solved using a lightweight heuristic approach. The proposed schemes are evaluated in offline and on-demand scenarios and compared against relevant state-of-the-art protocols. The results in the offline scenario show that the path planning strategy reduces the number of stops and the energy consumed by the mobile charger, compared to existing offline solutions. This is with further reduction in time complexity, due to the simple heuristics that are used. The results in the on-demand scenario confirm the effectiveness of the path planning strategy. More importantly, they show the impact of path planning, TTL and multi-node charging on the efficiency of handling the requests, in a way that reduces node failures and the mobile charger energy expenditure.

<italic>ESync</italic>: Energy Synchronized Mobile Charging in Rechargeable Wireless Sensor Networks

Recent years have witnessed many new promising technologies to power wireless sensor networks, which motivate some fundamental topics to be revisited. Different from energy harvesting, which generates dynamic energy supply, the mobile charger is able to provide a stable and reliable energy supply for sensor nodes and, thus, enables sustainable system operations. While previous mobile charging protocols focus on either the charger travel distance or the charging delay of sensor nodes, in this work, we propose a novel energy synchronized mobile charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of traveling salesman problem (TSP)-based solutions, when the nodes' energy consumption is diverse, we construct a set of nested TSP tours based on their energy consumption, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging request sequence of nodes with their sequence on the TSP tours. Experimentation and simulation demonstrate that ESync can reduce charger travel distance and nodes' charging delay by about 30% and 40%, respectively.

TADP: Enabling temporal and distantial priority scheduling for on-demand charging architecture in wireless rechargeable sensor Networks

Recently, adopting mobile energy chargers to replenish the energy supply of sensor nodes in wireless sensor networks has gained increasing attentions from the research community. The utilization of the mobile energy chargers provides a more reliable energy supply than systems harvesting dynamic energy from the surrounding environment. Wireless power transfer technique provides a new alternative for solving the limited power capacity problem for so many popular mobile wireless devices, and makes wireless rechargeable sensor networks (WRSNs) promising. However, mainly due to the underestimate of the unbalanced influences of spatial and temporal constraints posed by charging requests, traditional scheduling strategies for on-demand WRSNs architecture achieve rather low charging request throughput or successful rate, posing as a major bottleneck for further improvements. In this paper, we propose a TemporAl & Distantial Priority charging scheduling algorithm (TADP), which takes both the distance between nodes and the mobile charger and the arrival time of charging requests into consideration, and quantizes these two factors step by step. TADP forms a mixed priority queue which directs mobile charger to replenish the energy for nodes. At last extensive simulations are conducted to demonstrate the advantages of TADP. Simulation results reveal that TADP can achieve better scheduling performance in guaranteeing the scheduling success of the high-priority tasks and improving stability of the system.

Emergency Department Visits and Injury Hospitalizations for Female and Male Victims and Perpetrators of Intimate Partner Violence

Introduction. The potential for hospital-based interventions for male victims of intimate partner violence (IPV) as well as adult perpetrators of both genders has been largely unexplored despite early evidence of acute-care utilization that may be as high as female victims. The current investigation compared the emergency department (ED) and injury-related-hospitalization rates of IPV-involved individuals against standardized national norms, assessing differences by gender and victim/perpetrator-status. Methods. This cross-sectional study collected one-year ED and in-patient visit data from hospital records for individuals listed as victim or perpetrator in an IPV criminal charging request in a Midwestern county (N=2,937). Expected rates were calculated based upon age-adjusted national norms. Results. The IPV-involved population generated ED rates 4.1 times higher than expected and injury-related-hospitalization rates that were 4.0 times higher than expected. Bi-directionally-violent individuals (both victim and perpetrator in IPV charges) consistently had the highest utilization rates (ED 8.4 RR, injury-hospitalization 22.5 RR). Victims, primarily female, had higher ED-visits than perpetrators, primarily male (victims = 4.6 RR, perpetrator = 3.1 RR). Perpetrators, though, had higher injury hospitalizations (victims = 0.8RR, perpetrators = 5.5 RR). Conclusions. Substantial opportunities exist within acute-care medical settings to intervene with IPV-involved women, men, victims, and perpetrators, although the magnitude of the opportunity varied by setting, gender and victim/perpetrator-status.

Capacity Planning Frameworks for Electric Vehicle Charging Stations With Multiclass Customers

In order to foster electric vehicle (EV) adoption, there is a strong need for designing and developing charging stations that can accommodate different customer classes distinguished by their charging preferences, needs, and technologies. By growing such charging station networks, the power grid becomes more congested and, therefore, controlling charging requests should be carefully aligned with the available resources. This paper focuses on an EV charging network equipped with different charging technologies and proposes two frameworks. In the first framework, appropriate for large networks, the EV population is expected to constitute a sizable portion of the light duty fleets. This necessitates controlling the EV charging operations to prevent potential grid failures and distribute the resources efficiently. This framework leverages pricing dynamics in order to control the EV customer request rates and to provide a charging service with the best level of quality of service (QoS). The second framework, on the other hand, is more appropriate for smaller networks, in which the objective is to compute the minimum amount of resources required to provide certain levels of QoS to each class. The results show that the proposed frameworks ensure grid reliability and lead to significant savings in capacity planning.

Optimized Electric Vehicle Charging With Intermittent Renewable Energy Sources

Renewable energy and Electric Vehicles (EVs) are promising solutions for energy cost savings and emission reduction. However, integration of renewable energy sources into the electric grid could be a difficult task, because of the generation source intermittency and inconsistency with energy usage. In this paper, we present results of our study on the problem of allocating energy from renewable sources to EVs in a cost efficient manner. We have assumed that the renewable energy supply is time variant and in many ways unpredictable. EVs' charging requests should be satisfied within a specified time frame, which may incur a cost of drawing additional energy (possibly non-renewable energy) from the power grid if the renewable energy supply is not sufficient to meet the deadlines and may also reduce energy efficiency. We have formulated a stochastic optimization problem based on queuing model to minimize the time average cost of using non-renewable energy sources. The proposed approach fully considers the individual charging rate limit and deadline of each EV. The Lyapunov optimization technique is used to solve the problem. The developed dynamic control algorithm does not require knowledge of the statistical distribution of the time-varying renewable energy generation, EV charging demand, or extra energy pricing. Simulation results using different wind power generation profiles were performed and analyzed in the study. The results show that our EV charging scheduling method based on Lyapunov optimization can reduce both charging cost and mean delay time of fulfilling EV charging requests.

Applying a Dynamic Resource Supply Model in a Smart Grid

Dynamic resource supply is a complex issue to resolve in a cyber-physical system (CPS). In our previous work, a resource model called the dynamic resource supply model (DRSM) has been proposed to handle resources specification, management and allocation in CPS. In this paper, we are integrating the DRSM with service-oriented architecture and applying it to a smart grid (SG), one of the most complex CPS examples. We give the detailed design of the SG for electricity charging request and electricity allocation between plug-in hybrid electric vehicles (PHEV) and DRSM through the Android system. In the design, we explain a mechanism for electricity consumption with data collection and re-allocation through ZigBee network. In this design, we verify the correctness of this resource model for expected electricity allocation.

모바일 감성 센싱 단말기의 무선 충전을 위한 프로토콜 설계 및 구현

In order to supply emotion service depending on user's emotional change in a mobile environment, various researches have been carried. This paper discusses a protocol for wireless charging and an embedded platform of the mobile emotional sensing device which supports that. Wireless charging process relieves user's vexatious task to charge the emotional sensing device. To support wireless charging, there are one basestation and several mobile devices. Basestation coordinates and controls the devices over wireless communication, as well as supplies energy. For 1:N communication we defines the network whose superframe is classified into four categories: a network join superframe, a charging request superframe, a charging superframe and an inactive superframe. Physical layer provides how to supply energy to the devices and communicate physically. Mobile device is equipped with energy charged circuits, which correspond with the defined energy supplying method, as well as bidirectional communication circuits. Mobile device monitors and analyzes its own battery status, and is able to send a request packet to basestation. Therefore, it can be charged before its battery is exhausted without user's perception.