Charging Path Research Articles

The Combined Strategy of Energy Replenishment and Data Collection in Heterogenous Wireless Rechargeable Sensor Networks

The wireless energy transfer technology provides an effective solution of the energy shortage of wireless sensor networks. However, facing with complicated network areas and various kinds of applications, using one type of mobile devices and one data transmission mode: mobile sink mode or multihop transmission, cannot satisfy the requirements of the wireless rechargeable sensor networks (WRSN). To address this, a novel data collection and relay strategy is proposed. Under this strategy, considering various kinds of traffic requirements and limitations of sensor nodes, we propose a jointly scheduling of moving vehicles (MVs) and unmanned aerial vehicles (UAVs) to adapt to the complex environment and improve network performance. Through planning charging path and designing data transmission policy, the proposed algorithm prolongs the network lifetime while guarantees the quality of service of heterogeneous traffics. As the differences in moving speed, charging rate, and the amount of responsible data, there is some spare time of the MV or the UAV in a cycle and a global charging. A collecting algorithm is proposed to further charge and collect data from the cluster out of the original path. Finally, the results verify that the proposed algorithm can effectively reduce the dead time of the network and improve the energy efficiency simultaneously.

Logistics Pure Electric Vehicle Routing Based on GA-PSO Algorithm

Abstract:Based on current energy storage technologies such as batteries and fuel cells, the inherent battery capacity of electric vehicles puts constraints on their driving range and requires charging in the process of completing driving tasks. In this paper, with the current practical application in logistics industry as the background, from electric vehicle charging scheduling and path planning, a hybrid algorithm combining genetic-particle swarm algorithm is proposed to plan the best driving route for a group of electric logistics vehicles with vehicle load, vehicle battery life, charging facility location and customer time window as constraints and the total cost as the objective function. Based on the single distribution center, a more complex multi-distribution center electric vehicle path planning problem is considered. In this paper, multiple sets of Solomon VRPTW data sets are selected to test the prepared algorithm, and the results show that the algorithm can effectively plan the best distribution scheme.

Skin B/N-doped anatase TiO2 {0 0 1} nanoflakes for visible-light photocatalytic water oxidation

The limited visible-light-responsive photoactivities of most doped wide-bandgap photocatalysts with widened absorption range have long been the obstacles for the efficient conversion of solar energy to chemical energy by photocatalysis. The weak transport ability of visible-light-induced low-energy charge carriers, and numerous recombination centers arising from the energy-band modifiers along the transport path are two major factors responsible for such a mismatch. A potential solution is to shorten the transport path of photo-induced charges in well-modulated light absorbers with low-dimensional structure and the spatially concentrated dopants underneath their surfaces. As a proof of concept, skin B/N-doped red anatase TiO2 {001} nanoflakes with the absorption edge up to 675 nm were synthesized in this study. Experimental results revealed that boron dopants in the TiO2 nanoflakes from the hydrolysis of nanosized TiB2 played a crucial role in controlling nitrogen doping in the surface layer of the nanoflakes. As visible light excitation occurs at the surface layer, the photons can be sufficiently absorbed by the formed energy levels at the surface layers, and the photogenerated charge carriers can effectively migrate to the surface, thus leading to efficient visible-light-responsive photocatalytic oxygen evolution activity from water oxidation.

Charging scheme for wireless sensor network based on area scanning and energy detection

With the development of wireless sensor networks, the demand for wireless sensors increased sharply. At the same time, there is a problem that the storage of energy is relatively low due to the small size of wireless sensors, which means that it needs to replace when sensors are out of charge. It will cost huge finance and pollute the environment. Consequently, many scholars propose using wireless sensors that can recharge instead of primary wireless ones. In this case, choosing a suitable route to charge wireless sensors has become a significantly critical problem as there is a relationship between energy loss and the charging path. Since it is unstable or unsuitable to use solar energy and wind energy to charge, charging wireless sensors using drones, proposed by Kulaea T. Pauu1, Han Xu, and Bang Wang [1], has become a feasible solution. However, this solution still has some drawbacks as it only uses energy to charge the point with the lowest energy level by drones without considering the priority, the rest of the power, and the efficiency of time and energy. This report will use the data about the utilization rate of energy and make some comparisons to improve this solution.

Electric vehicle charging path planning considering couped faults in distribution-transportation network

Once a distribution network failure occurs, it can spread to the traffic network through the coupling point, causing electric vehicles (EVs) to change their charging paths. To address this problem, this paper presents an EV charging path planning approach that considers coupled faults in the distribution-transportation network. First, the cascading failure model of the distribution-transportation network and the model for choosing charging stations are presented to transfer the information of coupling faults propagation and coupling points power interaction to the follow-up path planning scheme. Second, a time occupancy road resistance model that considers congested and unobstructed traffic states is proposed to calculate the road section travel time, based on the analysis results of the evolution process of road traffic flow queuing using traffic wave theory. For the speed and density parameters in the traffic wave model, values are calculated using the logistics speed-density model and the time occupancy model. Third, a multi-objective optimization function that integrates travel cost and coupling network operation state is determined from the perspective of hindering the propagation of coupling faults. The function is solved to recommend optimal charging paths using an improved A* searching algorithm. Finally, a 90-bus road network and three 33-bus distribution networks are selected as examples to verify the veracity and validity of the proposed model and method. The research results demonstrate that the proposed method can alleviate traffic congestion.

Optimized Deep Reinforcement Learning for Smart Charging Scheduling of Plug-in Electric Vehicles

In recent decades, fossil fuels have been the major source of energy but concerns about price fluctuations, security, and environmental issues. Plug-in electric vehicles (PEVs) should be connected to the power grid for battery charging. With a wide range of cars, the power system performance in the distribution network. The uncontrolled charging of the battery can cause issues such as overvoltage, overload, unbalanced load, instability, harmonics, and increased loss. Hence, efficient charging methodologies need to be developed for the power grid. The objective of this article is to maximize the total revenue of the charging stations and schedule the charging stations for PEV charging with reduced cost. First, the system model with its objectives such as demand, constraints, and fitness values are computed. Next, efficient charging path planning is proposed using the deep Q network, which is a reinforcement learning model. This can assist the PEV user in selecting the charging station and planning its path. Lastly, the particle sailfish optimization from our previous research work has been used to solve the charging station optimal solution. The simulation results analysis shows the efficiency of the proposed model in reducing the charging cost and time and improves the charging scheduling of PEVs.

High-efficiency of novel hierarchical 3D macroporous g-C3N4 material on solar-driven photocatalytic water-splitting for hydrogen evolution

The use of multi-pore nanostructured g-C3N4 photocatalysts is an efficient approach to separate photogenerated charge carriers and increase visible light photocatalytic performance. Recent progress has yielded nanostructured material through hard templating, which limits potential applications. Integrating the 2D building block into multidimensional porous structures remains a significant challenge in scalable production. Herein, a novel technique based on P407 bubble clusters templating and fixation by freezing is described for the first time to fabricate a 3D opened-up macroporous g-C3N4 nanostructures for photocatalytic H2 evolution. Three-dimensional hierarchical nanostructures provide more contact active sites and synergistically promote the creation of heterogeneous catalytic interfaces. This feature is very useful for understanding the transfer path of photoinduced charges as well as the origins of the high charge separation efficiency in photocatalytic reactions, thus yielding a remarkable visible light-induced H2 evolution rate of 4213.6 μmol h−1 g−1, which is nearly 5.6 times (716 μmol h−1 g−1) higher than that of lamellar bulk g-C3N4. This newly developed approach offers a promising alternative to synthesize broad-spectral response 3D hierarchal g-C3N4 nanostructures and can be extended to assemble other functional nanomaterials as building blocks into macroscopic configurations coupled with electronic modulation strategy simultaneously.

UAV Dispatch Planning for a Wireless Rechargeable Sensor Network for Bridge Monitoring

Due to the breakthrough of wireless power transfer technology, wireless rechargeable sensor networks (WRSNs) have the potential to provide sustainable work. Most existing researches on WRSNs usually focus on the cases that mobile charging vehicle moves freely through the sensors. However, for some applications, such as bridge monitoring, WRSNs are implemented in a three-dimensional space with obstacles, so the charging path may be blocked by the obstacles. To cope with this problem, charging scheduling to replenish a wireless rechargeable sensor network for bridge monitoring by an unmanned aerial vehicle (UAV) is studied. The problem is formulated as an optimization problem through optimizing UAV navigation path and sensor energy allocation collaboratively. This optimization problem is hard to be solved as both path navigation and energy allocation are required to be optimized simultaneously. To circumvent this challenge, an improved ant colony system algorithm (IM-ACS) is proposed to plan the trajectory of the UAV between sensors. By integrating enhancement factors and dynamic pheromone intensity coefficients, the convergence of the algorithm is accelerated. Then, a two-stage algorithm is proposed to schedule charging sequence and assign energy with limited energy carried by the UAV in each charging period. Experiments and simulations show that the proposed approach achieves shorter feasible trajectory paths and longer network lifetime than those obtained by the compared methods.

Photovoltaic Performance of Perovskite Solar Cells and Their Continuous Charge Path Control in Electric Vehicles

Perovskite Solar Cells (PSCs) are new thin-film photovoltaic devices that have developed rapidly in recent years. It is considered a promising third-generation solar cell due to its high Monochromatic Incident Photon-to-electron Conversion Efficiency (IPCE), simple preparation process, and low cost. Firstly, the photoelectric characteristics of PSCs with four different structures and light-absorbing materials are analyzed. The results show that when CH3NH3PbI3 is used as the absorbing layer, the perovskite film with a porous structure has good crystallinity, and the photogenerated electron injection of TiO2 is more effective. The photoelectric Conversion Rate (CVR) of Pb-based organic cells with porous structure reaches 14.67%, and the photoelectric CVR of Pb-based organic cells with flat plate structure is only 6.35%. Then, a simple superimposed electrode type PSC is designed. Experiments have shown that the structure can greatly improve IPCE. The Spiro-MeOTAD layer should be retained. When the Layer one spraying amount is 250 μL, and the Layer two spraying amount is 500 μL, the IPCE obtained by the battery is the highest. The IPCE of the battery prepared by the ball milling method is higher than that of the battery prepared by the ultrasonic crushing method. Different substrate materials are compared. Using aluminum foil as the substrate can achieve the best photoelectric performance, and the corresponding photoelectric CVR reaches 11.62%. Based on the above test results, superimposed electrode-type PSCs are prepared and used in electric vehicle power generation devices. Super-Capacitor (SC) connection enables continuous charging in low-light conditions. The results show that the SC charges the designed superimposed electrode-type PSCs when the brightness decreases. The timing of charging and discharging is adjusted under the upper and lower limits of the threshold to ensure that the design PSCs remain charged.

Multi-UAV WRSN charging path planning based on improved heed and IA-DRL

Aiming at the problem of how to improve the energy utilization rate of UAV (Unmanned Aerial Vehicle) in the charging process of wireless rechargeable sensor network, a charging path planning scheme for multi-UAV wireless rechargeable sensor network based on deep reinforcement learning is proposed. Firstly, the multi-UAV path planning problem is described and a network model is established, and then the network model is optimized by using the improved dynamic clustering algorithm of HEED, and then an intelligent path optimization algorithm based on the intelligent algorithm and deep reinforcement learning IA-DRL is proposed for the problem model. According to this algorithm, the optimal charging path of multiple drones is obtained, and finally the drones charge each node to be charged in the network. The experimental results show, compared with other traditional heuristic methods, IA-DRL has more advantages in solving small and medium-scale UAV path planning problems, and compared with the neural network model without PSO, the minimum AVG performance of IA-DRL is improved by about 3.8% and has a faster convergence speed about 550 episodes.

A 24 V to 1 V integrated dual-charging path dual-inductor hybrid converter for improved step-up load transient response

This paper proposes a dual-charging-path dual-inductor hybrid converter that can charge two inductors simultaneously to improve the step-up load transient response. The proposed scheme only introduces one additional switch and capacitor to form a dual charging path during step-up load transient response. All power switches are integrated using 5 V transistors with 0.25 μm HV BCD process. Using type-3 PWM control combined with a fast load transient detector, simulation results show that the proposed circuit achieves 55.4% voltage drop and 23.6% recovery time reduction compared to the conventional DIH converter for load transient step from 20 mA to 4 A.

Electric Vehicle Charging Scheduling Optimization Method Based on Improved Ant Colony

The influx of electric vehicles significantly impacts the power grid. In the process of power grid operation, the operation of charging and discharging of electric vehicles can be used to dispatch the load of power grid operation. The optimization method of charging and dispatching electric vehicles based on improved ant colony is studied. Pheromones generated during the charging of the electric vehicle are obtained, and the charging limit range of the battery is delimited. The dynamic window constructs the charging scheduling model improved ant colony algorithm, and the optimal charging path is selected to complete the scheduling method design. The experimental results show that the proposed method can adjust the charging and discharge in the period of valley price and peak price, play the role of peak clipping, and realize the load regulation in different periods, which has application value.

Regenerative Charging Technique for Electrical Vehicles

Various sources of power generation techniques are in use in recent days. Automated vehicles have a tremendous future. Road transportation is majorly used transportation. Usage of car is drastically increased and the need for the petrol and diesel are increased. Now days due to these electric vehicles are started to implement in our country. Even government is started to encourage the electric vehicle developers. At a public parking facility, only electric vehicles (EVs) that are parked at dedicated parking spaces with charging points can enjoy charging services. Installing a charging point at each parking slot is very expensive. As an alternative, this project proposes the novel idea in a public transport facility, only electric vehicles (EVs) that can drive at dedicated with wireless charging path which can enjoy charging services. Installing a Wireless Charging Path (WCP) as Electric Vehicle Service Road (EVSR). Like service roads will be easy for charging the EVs wirelessly while traveling. Charging mechanism of vehicles is done via a wireless which reduces the requirement of cables, reduce the waiting time for charging and also has generating system based on solar energy. It is also based on ais artificial intelligence system.

A Novel Dual-Slope Resistance to Digital Converter With Lead Resistance Compensation

A novel dual-slope resistance to digital converter (RDC) for compensating lead resistance is presented. The RDC uses a dual-slope analog-to-digital converter (ADC) structure along with three switches and a diode. It completes measurement in one charging–discharging cycle only, hence provides an improvement to existing RDCs based on direct microcontroller interfaces (DMIs), where two or three charging–discharging cycles are required. Moreover, it incorporates the feature of lead resistance compensation in dual-slope RDCs. The scheme works by connecting the resistive sensor to the input terminal of the integrator of the dual-slope ADC. In the charging path (or mode), sensor resistance is presented along with a lead resistance; and in discharging path, sensor resistance is bypassed, and only lead resistance is there. The difference between the RC time constant during these two modes is used to find the sensor resistance. The diode is used to turn on a switch for bypassing the resistive sensor. The diode voltage drop is not there in the charging and discharging path, which is another novelty. A detailed error analysis is also carried to evaluate the performance of the scheme considering switch resistance, diode voltage drop, voltage mismatch, and opamp noises. The scheme works with low-value resistive sensors ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\approx 100~\Omega $ </tex-math></inline-formula> ), a feature not found in the existing dual-slope RDCs. The hardware prototype of the proposed scheme on the breadboard shows a measurement time of 2.09 ms with an average error of ±0.04% at 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> when lead resistance varied from 20 to 100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> . The scheme takes 23.44% less time for measurement.

Effect of Te doping on oxidation resistance and electronic structure of two-dimensional InSe

InSe is a typical two-dimensional (2D) layered semiconductor material, which has excellent electrical properties and moderate adjustable band gap. It is found that InSe has an attractive application prospect in optoelectronic devices. However, some studies have shown that InSe in a single selenium vacancy (Vse) system is easily degraded when exposed to the environment of O&lt;sub&gt;2&lt;/sub&gt; molecule, which seriously affects the application of InSe in the field of electronic devices. In order to improve the environmental stability of the material, the substitution doping method of Te is proposed in this work. Density functional theory (DFT) is used to analyze the electronic structure, adsorption energy, Bader charge and energy reaction paths of the different systems. It is found that Te substitution doping can significantly improve the stability of InSe. At the same time, the defect state produced by Vse can be eliminated. The specific research results are as follows. First, the dissociation barrier of O&lt;sub&gt;2&lt;/sub&gt; molecule on Te doped InSe surface (InSe—Te) is as high as 2.67 eV, indicating that Te-doped InSe has a strong antioxidant capacity. Second, the distance between O&lt;sub&gt;2&lt;/sub&gt; molecule and the surface of InSe—Te is 3.87 Å, and the adsorption energy is only –0.03 eV, indicating that O&lt;sub&gt;2&lt;/sub&gt; molecules are physically adsorbed on the monolayer surface. Third, Te doping not only improves the antioxidant capacity of the InSe, but also eliminates the defect state produced by Vse. Fourth, the Te-doping obviously eliminates the original Vse defect state or impurity band. The density of states and band structure of Te-doped InSe are almost the same as those of perfect InSe, which can maintain the stability of InSe structure and effectively reduce the damage of oxidation environment to defective InSe monolayer. The results of this study will be helpful in improving the environmental stability of InSe 2D material devices and promoting the research and development of InSe 2D devices.

Robot Memorial Path Planning for Smart Access of Indoor Distributed Charging Piles

Autonomous mobile service robots are used to complete many tasks, such as cleaning, transporting goods and monitoring. Such tasks usually require uninterrupted and continuous service. However, the battery of the robot is limited and must be charged frequently. For a large number of robots, it is essential to select a suitable charging pile. For this issue, we propose a path planning model for robots to intelligently access a limited number of charging piles distributed on the map. The traditional path planning model mainly considers the shortest path criterion to generate the path. Different from this, the path planning model in this paper not only considers the shortest path, but also the service position of the robot after charging, the remaining power of the robot, the state of the charging pile and the position of the robot in the map. Our path planning assigns the most suitable charging pile to the robot that needs charging. To solve the problem of high memory consumption and slow search speed when traditional A* algorithm is used for path planning, we propose local memorial path planning (LMPP) algorithm to quickly generate effective paths. The simulation results show that the proposed robot charging path planner can improve the robot service satisfaction and plan the effective path to the available charging piles.

Performance Analysis for Energy-Efficient Comparators

This work studies three different comparator proposed in recent years. Compared to the strnongARM latch as well as the Elzakker’s comparator, the dynamic comparator added cross-coupled devices that keeps the pre-amplifier’s integration nodes from fully discharge to the ground and thus improves energy performance. The dynamic bias latch-type comparator is an innovative design which added a tail capacitor to the pre-amplifier in order to block off the discharge route between the internal nodes and the ground. Though this design reduced the energy consumption significantly compared with the Elzakker’s comparator, it results in longer delay and only little improvement on the input-referred noise. The FIA dynamic comparator implemented a floating inverter amplifier (FIA) that utilizes a reservoir capacitor which cuts off its charging path and discharging path significantly reduced the energy consumption and improved noise performance to power itself.

Self-Powered Gate Drive Circuits With Optical Signal Isolation for In-Vehicle Marx Circuit of Ozonizer

Reducing the cost of a Marx circuit as a high-voltage power supply for nitrogen oxide (NOx) decomposition for diesel vehicles is required. The Marx circuit with MOSFETs has an advantage in size compared to the Marx circuit with gap switches. However, the Marx circuit with MOSFETs needs gate drive circuits on the gate of each MOSFET. The gate signal and drive power must be supplied to each gate with isolation in a low-cost way. However, the transformer for the isolation with high withstand voltage, such as 10 kV or more, is typically high cost because of the low demand. This article proposes the self-powered gate drive circuit using the charging path of the Marx circuit. The proposed gate drive circuit only requires a low withstand voltage because the power for the MOSFET on the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> th stage is supplied from the ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$n -1$ </tex-math></inline-formula> )th stage via a low-voltage transformer. Besides, the gate signal is transmitted through pairs of LEDs and photodiodes instead of high-cost optical fibers. The proposed gate drive circuits are developed and implemented into a prototype of the Marx circuit with an output voltage of 4 kV. From the experiments, it is demonstrated that the proposed drive circuits provide both drive power and gate signal.

On the Global Maximization of Network Lifetime in Wireless Rechargeable Sensor Networks

In a Wireless Rechargeable Sensor Network (WRSN), a mobile charger (MC) moves and supplies energy for sensor nodes to maintain the network operation. Hence, optimizing the charging schedule of MC is essential to maximize the network lifetime in WRSNs. The existing works only target the local optimization of network lifetime limited to MC’s subsequent charging round. The network lifetime has been normally reflected in a different metric that is not directly related to the final charging round period. To the best of our knowledge, this work is the first to address the global maximization of network lifetime in WRSNs, which optimizes not only the subsequent charging round but all charging rounds over the entire network lifetime. Another uniqueness is the joint consideration of both the charging path and charging time optimization problems. As a solution, we propose a genetic algorithm (GA)-based global optimization scheme that considers all the possible charging rounds. The GA has a novel mutation operation that mutates gene sizes for representing charging schedules with a varying number of charging rounds. The experiment results show that our algorithm can extend the network lifetime by 35.1 times on average and 38.6 times in the best case compared to existing ones.

Gas-dynamic losses in the flow part of the channel charge of a solid-propellant rocket engine

Modern methods of computational fluid dynamics have been used to study flows with mass supply in axisymmetric channels of solid-propellant rocket motor charges. The studies were carried out with the aim of increasing the accuracy of predicting the intraballistic parameters for performing engineering calculations. The analysis of changes in intraballistic characteristics in the flow part of the channel charge in the classical and nozzleless solid propellant rocket motors is carried out for different rates of mass supply from the combustion surface. For an isobaric combustion chamber, a characteristic velocity profile is shown along a tubular charge path and a charge with sudden expansion. It is shown that for a steady cosine profile of axial velocity after sudden expansion of the channel, a length of more than three gauges is required. For a high-speed combustion chamber, a comparison of the axial velocity profile is carried out depending on the flow velocity under different conditions of mass supply. The tendency of the influence of flow velocity on the character of the profile is noted. It is shown that at a Mach number over 0.5, an increase in the mass flow rate from the combustion surface provides a less filled velocity profile tending to cosine. The differences between the pressure losses in the flow passage of the channel charge, calculated in the axisymmetric approximation, and the losses determined using gas-dynamic functions, are shown. An increase of the mass flow rate in the charge channel of a nozzleless solid-propellant rocket motor leads to a decrease in pressure losses at Mach number over 0.8.