Recharge Stations Research Articles

A Two-Stage Approach for Electric Vehicle Routing Problem with Time Windows and Heterogeneous Recharging Stations

A Two-Stage Approach for Electric Vehicle Routing Problem with Time Windows and Heterogeneous Recharging Stations

Reliability modeling and assessment of a community microgrid with electric vehicle charging station as a critical load

Microgrids are constituted of interconnected loads and dispersed energy resources. They increase the reliability of the power supply, decrease the cost of the per unit energy and support the green environment concept. Microgrids have gained importance due to their ability to deliver reliable energy and operate in both grid-connected and off-grid/island modes. Renewable energy sources such as photovoltaic (PV) and wind can be connected to the microgrid system to generate green energy. An electric vehicle charging station (EVCS) can be deployed in microgrids powered by renewable energy sources. An effective way to handle the rapidly rising load demand of electric vehicles (EVs) is to integrate EV recharge stations into microgrids. Due to the intermittent nature of solar PV and EV load's reliability, an evaluation of microgrids becomes challenging. Assessment of the components’ failure and their impact on the system reliability is proposed using different methods. Reliability Block Diagram (RBD), Markov Reliability Modeling (MRM) and Fault Tree Analysis (FTA) - Monte Carlo simulation are used for the Reliability Modeling of a community microgrid considering EVCS as a critical load. The mean time to failure (MTTF) is computed for the entire microgrid which includes PV, wind and fuel cell systems integrated into the grid with associated power electronic devices, transmission lines, transformers and point of common coupling (PCC). MATLAB software was used to program and study the reliability aspects of the microgrid. Using the FTA MCS technique and grid functions for 45 years with 20 % reliability, significant results were obtained in terms of a MTTF rate of 26.16 years.

Index Air Quality Monitoring for Light and Active Mobility.

Light and active mobility, as well as multimodal mobility, could significantly contribute to decarbonization. Air quality is a key parameter to monitor the environment in terms of health and leisure benefits. In a possible scenario, wearables and recharge stations could supply information about a distributed monitoring system of air quality. The availability of low-power, smart, low-cost, compact embedded systems, such as Arduino Nicla Sense ME, based on BME688 by Bosch, Reutlingen, Germany, and powered by suitable software tools, can provide the hardware to be easily integrated into wearables as well as in solar-powered EVSE (Electric Vehicle Supply Equipment) for scooters and e-bikes. In this way, each e-vehicle, bike, or EVSE can contribute to a distributed monitoring network providing real-time information about micro-climate and pollution. This work experimentally investigates the capability of the BME688 environmental sensor to provide useful and detailed information about air quality. Initial experimental results from measurements in non-controlled and controlled environments show that BME688 is suited to detect the human-perceived air quality. CO2 readout can also be significant for other gas (e.g., CO), while IAQ (Index for Air Quality, from 0 to 500) is heavily affected by relative humidity, and its significance below 250 is quite low for an outdoor uncontrolled environment.

A route-based algorithm for the electric vehicle routing problem with multiple technologies

We consider a variant of the electric vehicle routing problem: a fleet of identical vehicles of limited capacity needs to visit a set of customers with given demands. An upper limit is imposed on the duration of the routes. Vehicles have limited autonomy: they may need to stop en-route at recharge stations. Recharges can be partial and multiple recharge technologies are available at stations, providing energy at different costs and different recharge rates.We present a new a branch-and-price algorithm, that relies on an extended formulation having one variable for each possible depot-to-depot route of each vehicle, implicitly encoding also recharge plans. We design ad-hoc pricing algorithms, which exploit a novel encoding of recharge plans, allowing for efficient bi-directional dynamic programming techniques.Extensive computational results show our approach to clearly outperform previous ones from the literature, being able to solve instances with up to 30 customers, 5 stations, 7 vehicles and 3 technologies to proven optimality within some minutes on a standard PC.

Thermal design of a system for mobile hydrogen powersupply

Ever more stringent emission regulations for vehicles encourage increasing numbers of battery electric vehicles on the roads. A drawback of storing electric energy in a battery is the comparable low energy density, low driving range and the higher propensity to deplete the energy storage before reaching the destination, especially at low ambient temperatures. When the battery is depleted, stranded vehicles can either be towed or recharged with a mobile recharging station. Several technologies of mobile recharging stations already exist, however, most of them use fossil fuels to recharge battery electric vehicles. The proposed novel zero emission solution for mobile charging is a combined high voltage battery and hydrogen fuel cell charging station. Due to the thermal characteristics of the fuel cell and high voltage battery (which allow only comparable low coolant temperatures), the thermal design for this specific application (available heat exchanger area, zero vehicle speed, air flow direction) becomes challenging and is addressed in this work. Experimental methods were used to obtain reliable thermal and electric power measurement data of a 30 kW fuel cell system, which is used in the Mobile Hydrogen Powersupply. Subsequently, simulation methods were applied for the thermal design and optimisation of the coolant circuits and heat exchangers. It is shown that an battery electric vehicle charging power of 22 kW requires a heat exchanger area of 1 m2, of which 60 % is used by the fuel cell heat exchanger and the remainder by the battery heat exchanger to achieve steady state operation at the highest possible ambient temperature of 43,6 °C. Furthermore, the simulation showed that when the charging power of 22 kW is solely provided by the high voltage battery, the highest possible ambient temperature is 42 °C. When the charging power is decreased, operation up to the maximum ambient temperatures of 45 °C can be achieved. The results of maximum charging power and limiting ambient temperature give insights for further system improvements, which are: sizing of fuel cell or battery, trailer design and heat exchanger area, operation strategy of the system (power split between high voltage battery and fuel cell) as well as possible dynamic operation scenarios.

Development of an Unmanned Mobile Current Turbine Platform

A prototype low-flow (~0.5 m/s) marine current turbine for deployment from a small unmanned mobile floating platform has been developed at Florida Atlantic University for autonomously seeking and harnessing tidal/coastal currents. The support platform is an unmanned surface vehicle (USV) in the form of a catamaran with two electric outboard motors and with capabilities for autonomous navigation. An undershot water wheel (USWW), aided by a custom flow concentrator, has been selected as the basic design for the marine current turbine, which is mounted on the stern of the USV. The concept of operation is that the unmanned surface vehicle would navigate to a designated marine current resource, autonomously anchor at the location, align itself in the current, and deploy the USWW turbine using a custom cable-lift deployment mechanism. As the USWW harnesses the local current, an onboard power-take-off (PTO) device converts the harnessed mechanical energy to electricity which is stored in onboard batteries. The selected PTO utilizes a spur drivetrain/gearbox coupled with a NuVinci Ball-continuously variable transmission (CVT). It is estimated that the small prototype turbine system will produce over 12W power for currents over 0.5 m/s. The automated anchoring system consists of an electric winch, a Rocna anchor, anchor chain/rode and a line locking mechanism designed to aid in taking tension off the winch. Preparations have been made to test and demonstrate the application of the platform in harnessing tidal currents in the Intracoastal Waterway in South Florida and coastal currents at locations off Fort Lauderdale, Florida. The preparations include obtaining the necessary environmental permits for conducting in-water testing; developing required mitigation measures in protecting local wildlife and their habitats; and identifying potential in-water test sites and surveying them for their suitability in terms of current resource, bottom type, water depth and local boat traffic. Application of the marine current turbine platform to serve as an unmanned mobile floating recharge station for small aerial drones will be demonstrated. For this purpose, the USV includes a flight deck for landing and takeoff of small aerial drones whose batteries would be recharged via a wireless direct-contact recharging pad powered by the onboard batteries. Modeling in support of turbine design and parametric studies in support of optimization of the performance of the system will be discussed. Scaling of the prototype system in terms of size and capacity will be discussed.

When Hierarchical Federated Learning Meets Stochastic Game: Toward an Intelligent UAV Charging in Urban Prosumers

Unmanned aerial vehicles (UAVs) nowadays are developing rapidly for various applications such as UAV taxis and delivery drones. However, the limited battery energy restricts the flight distance of the UAVs. Thus, urban prosumers equipped with drone recharge stations are introduced to provide charging services for the UAVs. In this article, firstly, a day-ahead energy scheduling problem for UAV charging-enabled urban prosumers is studied, where the objective is to maximize the overall energy satisfaction of the prosumers with ensuring the quality of service (QoS) of the charged UAVs. Specifically, to deal with the considered problem, we decompose it into two stages: 1) day-ahead energy requirement data prediction stage, and 2) energy scheduling stage per prosumer. Thus, secondly, a joint method based on hierarchical federated learning (HFL) on long short-term memory (LSTM) architecture (HFL-LSTM) and stochastic game-based multi-agent double deep Q-learning (MADDQN) with community agent-independent approach is proposed. In particular, the HFL-LSTM approach is leveraged to forecast each prosumer’s energy requirement data without centralized collecting local prosumers’ data such that to protect data privacy. Then, the stochastic game is adopted to analyze the formulated problem, aiming to find the Nash equilibrium strategy. Afterward, MADDQN with a community agent-independent method is utilized to achieve the best energy scheduling strategy per prosumer. Finally, the experimental results demonstrate the superiority of the proposed joint method that can achieve the lowest mean squared error with the value of 0.0152 and the highest energy satisfaction (36388) achieved by the Nash equilibrium policy compared with the benchmarks.

Impact Analysis and Energy Quality of Photovoltaic, Electric Vehicle and BESS Lead-Carbon Recharge Station in Brazil

The search for charging electric vehicles using renewable energy sources and ensuring the stability of the electrical system has been growing. This has led to the development of charging stations that integrate photovoltaic systems, energy storage systems in batteries, and charging stations. The storage system allows for applications that increase the reliability of the electrical network. The objective of this work is to study, based on real operational scenarios, the impacts, and criteria of energy quality of a charging solution containing a photovoltaic system, a storage system, and a charging station. In the solution, the batteries are lead-carbon type and photovoltaics have priority in energy supply, using excess generation to charge the batteries. In this aspect, the work presents relevant contributions to the study of energy quality. An improvement in energy quality is pointed out with the use of batteries, proving that even in the most diverse operating situations, such as partial shading of the photovoltaic modules, with 20% shading relative to the nominal power, charging is not impacted and reductions in distortions are achieved, with a 50% reduction in voltage imbalance, when the storage system is a voltage reference and reference to the microgrid.

Renewable and Sustainable Energy Status and their Applicability in Electric Vehicle Recharge Stations

Renewable and Sustainable Energy Status and their Applicability in Electric Vehicle Recharge Stations

GMIX: Graph-based spatial–temporal multi-agent reinforcement learning for dynamic electric vehicle dispatching system

The past decade has witnessed a significant growth of electric vehicles (EVs) deployment in public and private transportation sectors. Dynamic electric vehicle routing aims to plan the routes of EVs to serve dynamically generated customers’ requests while ensuring the battery level by visiting the recharge stations. This paper studies the dynamic electric vehicle routing problem with simultaneous pick-up and delivery and soft time windows (DEVRP-STW). We design a graph-based spatio-temporal multi-agent reinforcement learning (GMIX) framework consisting of a spatio-temporal graph attention network (ST-GAT) and a value decomposition-based multi-agent reinforcement learning algorithm (Graph-QMIX). Graph-QMIX derives a multi-agent soft policy gradient based on QMIX and Soft Actor–Critic and utilises a graph-based mixing network proposed to enhance the interaction of agents. Extensive experiments including a case study using real-world taxi data from New York City and a simulation study are conducted. The proposed GMIX outperforms the baseline algorithms on the request–response ratio (RRO), the average travelling distance per completed request (ATR), the out of energy ratio (OER) and the average waiting time per completed request (AWR).

A choice experiment on preferences for electric and hybrid cars in Istanbul

We analyze households’ preferences for electric and hybrid cars in Istanbul. We apply an adaptive choice-based conjoint approach that adjusts the survey in real time according to respondents’ initial responses. We find a low potential for electric cars but a significant latent demand for hybrid cars. Among the three urban policies considered, it is more effective to subsidize parking of electric cars and bridge and tunnel crossing fees of hybrid cars. Respondents’ valuations of these policies are about $2,070 in the life cycle of a car. We estimate that respondents are willing to pay $3.4, $14.8, and $20.7 to increase the driving range by 1 km for hybrid, conventional, and electric cars, respectively. They are willing to pay about $258 to decrease the recharge time of an electric car by 1 min or to make recharge stations more common so that their detour time to reach stations decreases by 1 min.

On-Road Efficient Wireless Charging System for Electric Vehicle

With the growing popularity of Electric Vehicles (EVs), the need for a simple and robust charging scheme has become necessary. Consumers are dissatisfied with the typical recharging method at home or in station parks because it takes a long time to recharge. On a highway, waiting for a place to recharge the vehicle is not always a good idea. The super-fast recharge station has received positive feedback from drivers in terms of charging time; nevertheless, this is not always recommended, particularly in terms of battery security and durability. WPT (Wireless Power Transfer) technology has many inherent advantages over traditional means of power transfer. It has been proposed for use in a wide range of applications, ranging from low power biomedical implants (several watts) to electrical vehicle chargers (several kilowatts) to railway vehicles (several megawatts), with efficiency up to 95% or higher in some prototype systems. Capacitive coupling, magnetic coupling, frequency resonance matching, microwaves, lasers, and ultrasound waves are all methods for transferring electric power across a specified distance through air. However, resonant magnetic coupling appeared to be the most practical and promising method to date, with resonant magnetic coupling being used in the majority of medium to high power WPT systems constructed to date. In this project, a wireless charging system for lightweight electric vehicle is designed, built and tested. The problem with electrical vehicles is that it requires too much time to recharge the battery. Dynamic Charging could be the much anticipated concept that replaces the conventional method of charging of battery and reduces the time taken to change the battery. A solution to directly charge EVs as they travel along the highway will eliminate the two biggest barriers, travelling range anxiety and cost. This project is carried out using MATLAB Simulink software for simulation of the circuit. This project based on future infrastructure of EV’s technology.

Branch-and-cut-and-price for the Electric Vehicle Routing Problem with Time Windows, Piecewise-Linear Recharging and Capacitated Recharging Stations

The Electric Vehicle Routing Problem with Time Windows, Piecewise-Linear Recharging and Capacitated Recharging Stations aims to design minimum-cost routes for a fleet of electric vehicles subject to intra-route and inter-route constraints. Every vehicle is equipped with a rechargeable battery that depletes while it transports goods along its route. A vehicle must detour to a recharging station to recharge before draining its battery. To approximate a real recharging process, the amount of energy restored is modeled as a piecewise-linear function of the time spent recharging. Furthermore, each station has a small number of chargers, and hence, when and where a vehicle can recharge must be scheduled around the availability of a charger. This interaction between vehicles does not appear in classical vehicle routing problems and motivates the development of new methods that can exploit the joint routing and scheduling structure. This paper proposes a branch-and-cut-and-price algorithm that designates the routing to integer programming using Dantzig–Wolfe decomposition and the scheduling to constraint programming using logic-based Benders decomposition. Experimental results indicate that this hybrid method solves 34% of the instances with 100 customers.

Design of a Wireless Drone Recharging Station and a Special Robot End Effector for Installation on a Power Line

Drone autonomous operations near power lines are growing steadily and require innovative techniques to keep them on air. This paper presents a novel electromechanical recharging station that can be mounted on energized AC power line to charge the drone battery wirelessly without a need to modify the electrical infrastructure. The work shows a thorough analysis of the electrical and mechanical core components to build a flexible, lightweight and efficient recharging station that can be attached to a robotic arm. The work also discusses the recharging station design and its special robot end effector that mechanically couples the station with an aerial manipulator. Finally, the recharging station has been tested in the lab and in a real power line setup to validate its design and efficiency. The total achieved mass is 2300 grams with a harvesting efficiency of 77% at 250 A primary current.

Multi-Objective Vehicle Routing Problem for a Mixed Fleet of Electric and Conventional Vehicles with Time Windows and Recharging Stations

The vehicle routing problem as a challenging decision problem has been studied extensively. More specifically, solving it for a mixed fleet requires realistic calculation of the performance of electric and combustion vehicles. This study addresses a new variant of the vehicle routing problem for a mixed fleet of electric and combustion vehicles under the presence of time windows and charging stations. A bi-objective mixed-integer programming model is developed which aims at minimizing cost and pollution level concurrently. To accurately quantify travel quantities, such as fuel consumption, emission, and battery charge level, a set of realistic mathematical formulas are used. The model is first converted to a single-objective counterpart using the epsilon-constraint method and a simulated annealing algorithm is tailored to obtain Pareto optimal solutions. A discussion is also made on how the final solution can be selected from the Pareto frontier according to the design objectives. The presented framework can find a set of Pareto optimal solutions as a trade-off between cost and pollution objectives by considering different combinations of electric and combustion vehicles. It was shown that those solutions that involve more electric fleet than combustion fleet, lead to higher total costs and smaller emissions and vice versa.

Efficient Aerial Data Collection With Cooperative Trajectory Planning for Large-Scale Wireless Sensor Networks

Due to the flexibility and agility, unmanned aerial vehicle (UAV) is a promising way for gathering data generated by wireless sensor networks. However, the limited battery capacity of the UAV restricts its application on many occasions, e.g., the networks deployed in the wild. In this paper, we propose a cooperative trajectory planning scheme to deal with the energy issue of the UAV, where a truck carrying backup batteries moves along with the UAV acting as a “mobile recharging station”. Our optimization task is to minimize the total mission time for gathering data from all the sensor nodes, which can be achieved by solving two problems: First, we need to divide the entire mission area into multiple subregions so that the UAV can hover over each subregion to collect the data of the sensor nodes through just one taking-off and landing under the constraint of battery capacity; second, we should find out the optimal trajectory of the truck so that the UAV can get to the hovering positions of each subregion from the truck and fly back to it before the battery drains considering the road condition in real world. We introduce an efficient clustering algorithm to partition the area into subregions in a load-balanced way to minimize the number of movements of the UAV. The trajectory planning task is formulated as a coordinated traveling salesman problem, which is solved by a three-step trajectory planning algorithm heuristically, and we also give the analysis of the upper bound and lower bound to demonstrate the performance guarantee. Numerical results show that our proposed scheme provides an effective and cost-efficient way for the data collection of large-scale wireless sensor networks in practical scenarios.

Coordinated routing of electric commercial vehicles with intra-route recharging and en-route battery swapping

A primary challenge in goods distribution using Electric Commercial Vehicles (ECVs) pertains to tackling their limited driving range. This paper proposes a multi-faceted approach towards increasing the driving range of ECVs by coordinating the options of: (i) intra-route recharging at an intermediate Recharging Station (RS), with (ii) synchronised en-route battery swapping services performed by Battery Swapping Vans (BSVs) at a pre-planned rendezvous time and space. We introduce and solve a variant corresponding to an Electric Vehicle Routing Problem with Time Windows, RSs and Synchronised Mobile Battery Swapping (EVRPTW-RS-SMBS). In the proposed model, route planning is carried out synchronously for two interdependent fleets, i.e., ECVs and BSVs, which work in tandem to complete the delivery tasks. To address methodological complications arising from the simultaneous consideration of intra-route recharging at RSs and the synchronised battery swapping on-the-fly, the paper develops a pre-optimisation procedure based on a Non-Dominated Path Identification (NDPI) algorithm that is used in deriving a significantly strengthened path-based formulation of the problem, and an efficient dynamic programming based heuristic algorithm. To gain practical insights on the economic and environmental added value and viability of the proposed logistics model, we compare different scenarios for goods distribution using ECVs in urban and regional levels in London and Southeast England, respectively. A set of numerical experiments are further performed to demonstrate the efficiency of the proposed algorithms. Our results indicate significant cost and emissions savings and an opportunity for going beyond last mile local deliveries using ECVs with the proposed logistics model.

Realization and Energy Optimization of a Recharging Station for Electric Vehicles with Fixed Storage and Photovoltaic Panels

In recent years, the number of work on smart grids has increased, in particular work on the issue of electric vehicles (EVs) charging, which will be a critical aspect of this in the coming years. The scientific research now offers effective tools to optimize energy flows within electricity grids and plan the load of EV fleets. However, the difficulties associated with the interoperability seem to be largely underestimated. The study conducted jointly by the IETR and Dropbird highlights the technological obstacles that significantly hinder the development of intelligent charging solutions, and tests the use of dynamic programming based algorithms to deal with these issues.

Eco-Audit of MOFs as H₂ Storage Materials for Vehicle Applications, Using Novel Refueling Model

Metal-organic frameworks (MOFs) are a heavily researched candidate for fuel-cell electric vehicle (FCEV) hydrogen storage. However, little analysis has been done on the environmental impact of potential MOF vehicles compared to established alternative vehicles, such as compressed hydrogen or battery-electric vehicles. In this work, a preliminary eco-audit was conducted for a FCEV using an MOF hydrogen storage system based the best current MOF Ni₂(m-dobdc) (Ni-MOF-74). (1) Cost and environmental impact analyses were performed for both the production and use phases of an MOF-FCEV. The cost and environmental impact of MOF production was compared to that of Lithium Nickel Manganese Cobalt Oxide (LiNMCO) batteries, the current state-of-the-art for BEVs. (2) Environmental impact was assessed using embodied energy estimates based on reported values for LiNMCO BEVs. These highlighted MOF vehicles as a competitor to current renewable energy vehicle technologies. For the use phase, a hydrogen refueling station that produces hydrogen onsite by proton exchange membrane (PEM) electrolysis from grid electricity was compared to an equivalent population of battery electric vehicles (BEVs) charged at distributed recharging stations. FCEVs using the proposed refueling model were able to compete with BEVs both in terms of electricity CO₂ footprint and cost in the simulated solar-dominated Californian grid, but not in the hydro- and nuclear-heavy Ontario grid.

Matheuristics for Multi-UAV Routing and Recharge Station Location for Complete Area Coverage.

This paper presents matheuristics for routing a heterogeneous group of capacitated unmanned air vehicles (UAVs) for complete coverage of ground areas, considering simultaneous minimization of the coverage time and locating the minimal number of refueling stations. Whereas coverage path planning (CPP) is widely studied in the literature, previous works did not combine heterogeneous vehicle performance and complete area coverage constraints to optimize UAV tours by considering both objectives. As this problem cannot be easily solved, we designed high-level path planning that combines the multiobjective variable neighborhood search (MOVNS) metaheuristic and the exact mathematical formulation to explore the set of nondominated solutions. Since the exact method can interact in different ways with MOVNS, we evaluated four different strategies using four metrics: execution time, coverage, cardinality, and hypervolume. The experimental results show that applying the exact method as an intraroute operator into the variable neighborhood descent (VND) can return solutions as good as those obtained by the closest to optimal strategy but with higher efficiency.