Proposed Charging Scheme Research Articles

More efficient charging of ferroelectrets via tuning of the Paschen breakdown

Charging by means of dielectric barrier discharges (DBDs) is a critical step in the preparation of ferroelectrets (also called piezoelectrets). Triggering of the DBD charging in ferroelectrets is governed by Paschen's law. The Paschen-breakdown voltage Vb of common gases exhibits a U-shaped dependence on the product of gas pressure p and electrode spacing d (internal-cavity height in the present ferroelectret context). This allows for a more efficient charging strategy for ferroelectrets whose pd value at atmospheric pressure is different from the (pd)min value of the Paschen-breakdown curve. For easier triggering of the DBD charging, the pd is adjusted toward (pd)min (corresponding to Vb, min of the Paschen curve) by controlling the gas pressure in the cavity. Subsequently, pd is restored to its value at ambient pressure under the charging voltage in order to obtain a high remanent polarization. The proposed charging scheme is easy to operate and can significantly improve the charging efficiency of ferroelectrets with a wide range of cavity geometries.

A novel consumer-friendly electric vehicle charging scheme with vehicle to grid provision supported by genetic algorithm based optimization

Due to escalating demand for electric vehicles (EVs) in the worldwide transportation sector, the charging facilities supported by an efficient charging scheme across the power network have a significant impact on the operation of the power system. The unsupervised and decentralized charging of the EVs have several adverse effects on various operational features of the power system. On the other hand, the centralized charging system with proper charging schemes offers less complexity in power system operation to avoid unnecessary power system network stress. This article proposes a valley-filling technique-based EV charging scheme for residential consumers facilitated by a centralized charging system. The charging scheme comprises a genetic algorithm-based optimization for EV acceptance to utilize the available energy of the supply network in the best way. The charging scheme also includes vehicle to grid (V2G) facilitation and the reallocation technique for shifting EVs to resolve any overloading of charging slots. Simulation results of Genetic Algorithm (GA) based optimization show the attainment of near-optimum EV data set with less than 50 iteration numbers and usage of more than 99.5% of available kWh for charging. Other result segments indicate the efficient use of available supply energy by incorporating EVs in the valley time slab of the demand curve. Also, the findings point out that the charging scheme successfully encounters unwanted network stress issues during EV integration by V2G provision and reallocation technique, prioritizing consumer satisfaction at the overloaded charging time slots. The test case simulation results indicate that the ‘average to peak’ demand ratio in the demand curve is increased to 90% from 68% by incorporating the proposed charging scheme for EV charging. Moreover, the outcomes indicate the novelty of the proposed methodology in allocating charging slots considering complex driving parameters like network stress and customer prioritization.

A Novel Charging Method for Underwater Batteryless Sensor Node Networks.

In this paper, we present a novel charging method for underwater batteryless sensor node networks. The target application is a practical underwater sensor network for oceanic fish farms. The underwater sections of the network use a wireless power transfer system based on the ISO 11784/11785 HDX standard for supplying energy to the batteryless sensor nodes. Each sensor has an accumulator capacitor, which is charged for voltage supplying to the sensor node. A new distributed charging scheme is proposed and discussed in detail to reduce the required time to charge all sensor nodes of the underwater sections. One important key is its decentralized control of the charging process. The proposal is based on the self disconnection ability of each sensor node from the charging network. The second important key is that the hardware implementation of this new feature is quite simple and only requires to include a minimal circuitry in parallel to the current sensor node antenna while the rest of the sensor network remains unaltered. The proposed charging scheme is evaluated using real corner cases from practical oceanic fish farms sensor networks. The results from experiments demonstrate that it is possible to charge up to 10 sensor nodes which is the double charging capability than previous research presented. In the same conditions as the approach found in the literature, it represents reaching an ocean depth of 60 m. In terms of energy, in case of an underwater network with 5 sensors to reach 30 m deep, the proposed charging scheme requires only a 25% of the power required using the traditional approach.

Predicting-Scheduling-Tracking: Charging Nodes With Non-Deterministic Mobility

In Wireless Sensor Networks (WSN), the energy problem plays the critical role in network performance and lifetime, because of the limited battery capacities of sensor nodes. Recently, emerging wireless charging technologies provide a promising approach to address the energy problem in WSN. Researchers construct Wireless Rechargeable Sensor Networks (WRSN), which introduce mobile chargers with high capacity batteries to charge sensor nodes. Most studies in WRSN have paid attention to charging static nodes or mobile nodes with deterministic trajectories. In this work, we explore how to charge nodes with non-deterministic mobility. We propose a novel approach, named Predicting-Scheduling-Tracking (PST), to perform charging tasks in this case. In the proposed scheme, different from the existing work, we guide the mobile charger to chase the sensor and recharge it. In our work, the base station runs an improved LSTM to predict the future locations of nodes periodically. Then, the mobile charger can select an appropriate node as the charging target by a charging scheduling algorithm. During the energy transferring, a Kalman-filter-based tracking algorithm is used to ensure the charging-required distance between the mobile charger and the target node. The simulation results show that the proposed charging scheme can fulfil the charging tasks in WRSN of nodes with non-deterministic mobility.

Game Theory Based Charging Solution for Networked Electric Vehicles: A Location-Aware Approach

The recent explosive adoption of electric vehicles (EVs) and plug-in hybrid EVs (PHEVs) has sparked considerable interest in academia in developing efficient charging schemes. Supported by the advanced vehicle-to-grid network, vehicles and charging stations can, respectively, make better charging and pricing decisions via real-time information sharing. In this paper, we study the charging problem in an intelligent transportation system, which consists of smart-grid enabled charging stations and networked EVs. Each vehicle aims to select a station with the lowest charging cost by considering the charging prices and its location, while the objective of a charging station is to maximize its revenue, given the charging strategy of the vehicles. We employ a multi-leader multi-follower Stackelberg game to model the interplay between the vehicles and charging stations, in which the location factor plays an important role. We show that the equilibrium of the followers' subgame played by the vehicles exits, while the stations are able to reach an equilibrium of their subgame with respect to the charging prices. Therefore, the Nash equilibrium of the Stackelberg game is achievable using the proposed charging scheme. Finally, the performance of the proposed approach is demonstrated via extensive trace-driven simulations.

Plug-in electric vehicle charging management via a distributed neurodynamic algorithm

In this paper, an innovative electric vehicle (EV) charging scheme is proposed, which is the first time to take into full account three charging modes in one framework, i.e. slow charging, quick charging and battery swapping. The aim is to minimize the charging cost to achieve valley filling effect. Furthermore, in order to address the proposed charging scheme effectively a distributed neurodynamic algorithm is proposed to achieve an intelligent and optimal strategy. Compared with the centralized method, the distributed algorithm can better satisfy the requirement of EV charging environment in a distributed manner. In the simulation study, the effectiveness and convergence of our proposed algorithm are verified by case studies. In addition, the rationality and superiority of our proposed scheme and algorithm are also further validates by theoretical analysis.

Real-Time Smart Charging of Electric Vehicles for Demand Charge Reduction at Non-Residential Sites

Smart electric vehicle (EV) charging deals with increasing demand charges caused by EV load on EV supply equipment (EVSE) hosts. This paper proposes a real-time smart charging algorithm that can be integrated with commercial & industrial EVSE hosts through building energy management system or with utility back office through the advanced metering infrastructure. The proposed charging scheme implements a real-time water-filling algorithm able to reduce the peak demand and to prioritize EV charging based on the data of plugged-in EVs. The algorithm also accommodates utility and local demand response and load control signals for extensive peak shaving. Real-world EV charging data from different types of venues are used to develop and evaluate the smart charging scheme for demand charge reduction at medium & large general service locations. The results show that even at constrained venues such as large retails, monthly demand charges caused by EVs can be reduced by 20%–35% for 30% EV penetration level without depreciating EVs’ charging demand.

Joint Charging Tour Planning and Depot Positioning for Wireless Sensor Networks Using Mobile Chargers

Recent breakthrough in wireless energy transfer technology has enabled wireless sensor networks (WSNs) to operate with zero-downtime through the use of mobile energy chargers (MCs), that periodically replenish the energy supply of the sensor nodes. Due to the limited battery capacity of the MCs, a significant number of MCs and charging depots are required to guarantee perpetual operations in large scale networks. Existing methods for reducing the number of MCs and charging depots treat the charging tour planning and depot positioning problems separately even though they are inter-dependent. This paper is the first to jointly consider charging tour planning and MC depot positioning for large-scale WSNs. The proposed method solves the problem through the following three stages: charging tour planning, candidate depot identification and reduction, and depot deployment and charging tour assignment. The proposed charging scheme also considers the association between the MC charging cycle and the operational lifetime of the sensor nodes, in order to maximize the energy efficiency of the MCs. This overcomes the limitations of existing approaches, wherein MCs with small battery capacity ends up charging sensor nodes more frequently than necessary, while MCs with large battery capacity return to the depots to replenish themselves before they have fully transferred their energy to the sensor nodes. Compared with existing approaches, the proposed method leads to an average reduction in the number of MCs by 64%, and an average increase of 19.7 times on the ratio of total charging time over total traveling time.