Mobile Charging Station Research Articles

Cooperative Path Planning for Persistent Surveillance in Large‐Scale Environment with UAV‐UGV System

AbstractIn this paper, we consider the path planning problem of the Unmanned Air‐Ground Vehicle (UAV‐UGV) system for large‐scale environmental persistent surveillance. The goal is to acquire a set of periodically visited surveillance nodes while minimizing the traveling distance. Some expected key problems are the limitations of UAV speed, UGV speed, UAV endurance, and UAV field of view. To address this issue, the path planning of UAV‐UGV surveillance system is modeled as a TSP optimization problem based on multiple constraints, aiming to minimize the total path length of the system to perform the task. UAV is responsible for visiting the surveillance node and UGV serves as a mobile charging station. And a two‐layer chaotic aptenodytes forsteri optimization algorithm (Two‐CAFO) is proposed to solve this problem. Our solution has been tested in several simulated and real‐world environments. The results demonstrate that the proposed Two‐CAFO has superior performance compared to other state‐of‐the‐art algorithms in solving the path planning problem for large‐scale environmental persistent surveillance tasks of UAV and UGV. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Design and optimization of energy supplying system for electric vehicles by mobile charge stations

Electric vehicles (EVs) have been recommended worldwide as an alternative to internal combustion engine (ICE) vehicles. However, it will be difficult to supply enough energy to EVs using existing fixed charging stations (FCSs) and thus a mobile charging station (MCS) is proposed which has the advantage of being able to quickly and inexpensively provide charging services at any time and place. In this study, an energy supply system using MCS has been designed to verify a business model for South Korea. First, the entire country was divided into eight areas, and the population density center of each district was calculated. Second, BCS candidate sites were selected based on the population density center and EV numbers in each district. Third, by designing a business model, optimization was performed to maximize the overall profit. The result was calculated to meet the constraints such as meeting the local electricity demand and generate an annual profit of 224 million dollars. It is also predicted that this revenue could increase by 1959% by 2035 as the number of EVs increases. These results show that MCS can generate sufficient profits as a business and contribute to reduce carbon emissions.

Mobile charging station for electric tractors

The paper examines the issues of providing electrical energy to tractors operating on electric traction. Problems associated with the lack of infrastructure necessary for charging and recharging traction batteries are described. The rationale for the operation of electric tractors in agriculture is given. The article provides an overview of the available types of charging stations, their advantages and disadvantages, as well as some technical characteristics. The development and use of mobile charging stations for charging and recharging traction batteries of electric tractors directly in the field is substantiated. The main results provide information about the designed mobile charging station. The article contains a description of the design and composition of a mobile charging station, through which traction batteries are charged directly in the field. The rationale for choosing the type of charging station and the method of supplying it with electrical energy is presented. Particular attention is paid to the rational choice of storage batteries that will be located in the charging station. An assessment of the used rechargeable batteries is carried out, their main advantages and disadvantages are given, as well as the type of batteries chosen for use is justified.

Perceptions and preferences of patient services in emergency departments in Jeddah: a comprehensive survey

Objective: This study investigated the preferences and perception of the participants regarding patient services in Emergency Department (ED). Methods: A vast comprehensive survey, targeting a sample size of 597 participants in the Jeddah region, was conducted. The study aimed to understand the appeal and necessity of various features ranging from amenities in the waiting area, specialist care, medical staff professionalism, infrastructural modifications, and procedural changes in the ER departments. Results: A substantial number agreed on a free coffee/tea service (80%), free Wi-Fi (92.1%), long comfortable seats (84.9%), and a mobile charging station (92%). Interestingly, patients preferred health information on TV (52.5%). Understanding the need for additional amenities, 72% supported the idea of a dedicated play area for children. Almost 73.8% responded in favor of a valet parking service, and 69.9% recognized the importance of personal care services provided by medical staff and nurses. The survey further showed that the presence of a device indicating approximate waiting time was a desired feature (93%). Notably, the qualifications of the emergency physician mattered for 71.7% of participants. Concerning medication delivery, 70.2% of participants preferred receiving them at their location in the emergency department. Further, 71.1% preferred to have healthy snacks on waiting area, and about 98.2% needed toilets to be cleaned frequently and scented. Conclusion: This comprehensive survey indicated a significant inclination towards patient-centric modifications in the EDs. Patients valued informational, infrastructural, and service-oriented features, emphasizing the need for a personalized, comfortable, and informed experience.

Dynamic capacitated facility location problem in mobile renewable energy charging stations under sustainability consideration

The deployment of mobile renewable energy charging stations plays a crucial role in facilitating the overall adoption of electric vehicles and reducing reliance on fossil fuels. This study addresses the dynamic capacitated facility location problem in mobile charging stations from a sustainability perspective. This paper proposes Two-stage stochastic programming with recourse that performs well for this application, and the location of the mobile renewable energy charging station (MRECS) management addresses the complex dynamics of reusable items. To solve this problem, we suggested dealing with differential evolutionary (DE) and DE Q-learning (DEQL) algorithms, as two novel optimization and reinforcement learning approaches, are presented as solution approaches to validate their performance. Evaluation of the outcomes reveals a considerable disparity between the algorithms, and DEQL performs better in solving the presented problem. In addition, DEQL could minimize the total operation cost and carbon emission by 7% and 20%, respectively. In contrast, the DE could decrease carbon emissions and total operation costs by 5% and 2.5%, respectively.

Electric vehicle charging scheduling with mobile charging stations

Mobile charging station (MCS) is a brand-new technology to electric vehicle charging, which is a supplementary service for addressing the shortcomings associated with fixed charging station (FCS), such as prolonged waiting time of charging, charging congestion of FCS, and user travel anxiety. While MCSs offer convenience to electric vehicle users, the challenge faced in the MCS operation is that MCS equipped with large batteries to provide charging services to users by visiting them one by one not only leads to high operating costs, but also prolongs the waiting time for users to utilize MCS services. To overcome the issues, a novel framework is proposed by optimizing the MCS operation in economic efficiency. In this study, a variant of mixed integer linear programming (MILP) model is developed to maximize the MCS operator's profits with considering the user's perspective, including clustering, and covering user demands, setting temporary charging location for users, dispatching MCSs for charging and discharging, and scheduling EV charging. As the scale of the problem increases, the solution time of using the proposed MILP model is inefficient. Whereas an improved genetic algorithm is developed for solving a large-scale of the proposed model. Solomon's benchmarking instance data is adopted to evaluate the performance and validity of the proposed algorithm, complemented by various sensitivity analyses aimed at providing managerial insights into MCS operations.

Electric vehicle charging through mobile charging station deployment in coupled distribution and transportation networks

The interest in electric vehicles (EVs) has prominently increased in the recent years due to their state-of-the-art and environmentally-friendly structure. The fixed charging station (FXCS) investments, however, have lagged behind this increase, leading to many challenges for both the system operator (SO), such as power system reliability and stability, and the EV users, such as long charging times and range anxiety. In order to reduce the burden of FXCSs, in this study, mobile charging stations (MCSs) are considered and a novel energy management strategy is proposed for their optimal operation from the perspective of both the SO and EV users. The proposed optimization algorithm minimizes the total operation cost of the MCSs, which serve EVs located throughout the city, while accounts for the battery degradation cost of the MCSs. Furthermore, the proposed approach ensures the fairness between the MCS and FXCS operations by utilizing the Time-of-Use (TOU) tariff prices. The results are discussed in detail based on the operational costs, waiting time and power system impacts.

Second-Life Batteries Modeling for Performance Tracking in a Mobile Charging Station

Lithium-ion batteries are seen as a key element in reducing global greenhouse gas emissions from the transport and energy sectors. However, efforts are still needed to minimize their environmental impact. This article presents a path towards a circular economy and more sustainable batteries, thanks to their reuse in mobile charging stations for electric vehicles. This work presents the results of characterization tests and modeling of second life batteries. The presented characterization test and electrical models can be used as references to evaluate the performance of aged batteries after their first life. Detailed test procedures and data results are provided in an open-access data paper.

Mobile Charging Station Placements in Internet of Electric Vehicles: A Federated Learning Approach

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) can be deployed to complement fixed charging stations. Currently, the strategy of MCSs is to move towards the EVs with insufficient energy (IEVs) only after being requested, which is not efficient. However, similar to online car-hailing services, more IEVs could be charged and the charging expenses could be reduced if idle MCSs can actively move towards the potential charging positions. In this paper, the problem of placements of idle MCSs in an IoEV is investigated in order to enhance the proportion of charged IEVs and reduce the charging expenses of IEVs. To this end, we propose a Federated Learning based Placement Decision Method of Idle MCSs (FL-PDMIM) to help the idle MCSs to predict the future charging positions, by exploiting the historical routes of MCSs which contain rich information regarding the charging demand of IEVs. In the proposed framework, the historical routes are trained locally by each MCS, and then the local model parameters and charging records are periodically uploaded to an edge server for a global parameter aggregation. Then, idle MCSs decide their placements according to the predicted charging positions (potential charging positions). The training time can be largely shortened, because the distributed learning on each MCS is executed in parallel. Extensive simulations and comparisons demonstrate the performance superiority of FL-PDMIM. Specifically, with the proposed federated learning-based predictions, the waiting time of IEVs to be served can be significantly shortened, and FL-PDMIM enhances the proportion of charged IEVs and reduces the charging expenses of IEVs effectively.

Development and application of mobile charging station complex under multi-scenario charging requirements

Aiming at the limitations of current fixed charging facilities and considering the charging needs of users in multiple scenarios, a mobile charging complex with real-time monitoring of rest, consumption and charging safety has been developed to meet the needs of users in multiple scenarios. Firstly, according to the development status of mobile charging, the technical route of mobile charging complex is proposed and the functions and requirements of the complex are clarified. Secondly, the mobile charging complex device that can be used in multiple scenarios such as high-speed service areas, residential areas and so on. A mobile charging integrated cabin, a charging cabin and a rest cabin are respectively designed. And the functional composition and commercial promotion value of each cabin are researched, which has been applied in Quzhou service area of G60 Shanghai–Kunming expressway in Zhejiang, China. Finally, the development of mobile charging complex is prospected from the perspectives of ergonomics, demand response, intelligent parking and bionic charging arm. Through the practical application, it is verified that the technical solution can effectively reduce the operation and maintenance cost of charging facilities under the premise of safety and reliability. It realizes peak shaving and valley filling, which is conducive to the healthy and stable development of charging facilities.

Techno-economic assessment of hydrogen refueling station: A case study in Croatia

Hydrogen refueling station (HRS) capacity and location depend on the users, which makes it difficult to select the most favorable option before potential users are actually identified. As in Croatia, at least for now, there are no hydrogen users, this study considers a wide range of HRS capacities and their different configurations. These include hydrogen production and charging station within one existing wind farm in Croatia or both nearby the users, the hydrogen production within the wind farm and the charging station nearby the users, while hydrogen is delivered to the station with a tube trailer, and configuration of hydrogen production within the wind farm with a mobile charging station in case of several users in different locations. Each HRS configuration is evaluated by the obtained levelized cost of hydrogen depending on the capital, and operation and maintenance costs within the HRS techno-economic analysis provided.

Optimal Energy Management Framework for Truck-Mounted Mobile Charging Stations Considering Power Distribution System Operating Conditions.

A high charging demand from many electric vehicles (EVs) at a fixed charging station (FCS) with a limited number of charging poles can increase the waiting time of EVs and yield an abnormal power grid condition. To resolve these challenges, this paper presents an optimization framework in which a mobile charging station (MCS) is dispatched to the overloaded FCS to reduce the number of waiting EVs while maintaining normal power grid operation. Compared to existing MCS scheduling methods that do not consider actual power distribution system operations, the proposed framework takes into account the (i) active/reactive power flow and consumption of EVs, (ii) reactive power capability of MCS, and (iii) voltage quality in power distribution systems. In coupled transportation and power distribution systems, the proposed algorithm conducts optimal operation scheduling of MCS for both road routing and charging and discharging, thereby leading to the reduction of waiting EVs within the allowable voltage range. The proposed MCS optimization algorithm was tested in IEEE 13-bus and 33-bus distribution systems coupled with 9-node and 15-node transportation systems, respectively. The test results demonstrate the effectiveness of the proposed algorithm in terms of number of waiting EVs, voltage magnitude deviation, and reactive power of the MCS.

Optimal Management of Mobile Battery Energy Storage as a Self-Driving, Self-Powered and Movable Charging Station to Promote Electric Vehicle Adoption

The high share of electric vehicles (EVs) in the transportation sector is one of the main pillars of sustainable development. Availability of a suitable charging infrastructure and an affordable electricity cost for battery charging are the main factors affecting the increased adoption of EVs. The installation location of fixed charging stations (FCSs) may not be completely compatible with the changing pattern of EV accumulation. Besides, their power withdrawal location in the network is fixed, and also, the time of receiving the power follows the EVs’ charging demand. The EV charging demand pattern conflicts with the network peak period and causes several technical challenges besides high electricity prices for charging. A mobile battery energy storage (MBES) equipped with charging piles can constitute a mobile charging station (MCS). The MCS has the potential to target the challenges mentioned above through a spatio-temporal transfer in the required energy for EV charging. Accordingly, in this paper, a new method for modeling and optimal management of mobile charging stations in power distribution networks in the presence of fixed stations is presented. The MCS is powered through its internal battery utilizing a self-powered mechanism. Besides, it employs a self-driving mechanism for lowering transportation costs. The MCS battery can receive the required energy at a different time and location regarding EVs accumulation and charging demand pattern. In other words, the mobile station will be charged at the most appropriate location and time by moving between the network buses. The stored energy will then be used to charge the EVs in the fixed stations’ vicinity at peak EV charging periods. In this way, the energy required for EV charging will be stored during off-peak periods, without stress on the network and at the lowest cost. Implementing the proposed method on a test case demonstrates its benefits for both EV owners and network operator.

A hub-and-spoke architecture for a parcel delivery system using the cross-docking distribution strategy

This article develops a hub-and-spoke architecture for a parcel delivery system using a network that includes a distribution centre and several cross-dock facilities. Several real-world assumptions, including an electric truck fleet, mobile charging station, third-party logistics, capacity constraints, last-mile deliveries and customer dissatisfaction, are incorporated in this problem. Besides, because of the high complexity of the problem, a novel matheuristic algorithm based on the combination of the fix-and-relax algorithm (FARA) and genetic algorithm (GA), namely Mb-FARGA, is developed to solve large-sized instances. A comprehensive computational analysis is accomplished to validate the proposed mathematical models and evaluate the performance of the Mb-FARGA method. According to the results, the Mb-FARGA method demonstrates a very favourable performance. Moreover, the results show that considering a distribution centre with appropriate capacity in the cross-docking system is an excellent strategy to enhance customer satisfaction, improve the system’s performance and reduce capacity shortages.

Development of multiple plug-in electric vehicle mobile charging station using bidirectional converter

Electric vehicle (EV) charging station powered by the scattered energy sources with DC Nanogrid (NG) provides an option for uninterrupted charging. The NG powered by the renewable energy sources (RES) of photovoltaic (PV) and wind energy. When the excess power produced by the renewable energy stored in the local energy storage unit (ESU) utilized during shortage power from the renewable sources. During the overloading of NG and demand of energy in ESU; the mobile charging station (MCS) provides an uninterrupted charging. The MCS provides an option for battery swapping and vehicle to grid feasibility. The MCS required to monitor the state of charge (SOC) and state of health (SOH) of the battery. Monitoring of SOC and SOH related to the various battery parameters like voltage, current and temperature. A laboratory prototype is developed and tested the practical possibility of EV to NG and Internet of things (IoT) based monitoring of battery parameters.

An Optimization Model for the Temporary Locations of Mobile Charging Stations

A possible solution with which to alleviate the range anxiety of electric vehicle (EV) drivers could be a mobile charging station which moves in different places to charge EVs, having a charging time of even half an hour. A problem that arises is the impossibility of charging in any location due to heavy traffic or limited space constraints. This paper proposes a new operational mode for the mobile charging station through temporarily stationing it at different places for certain amounts of time. A mathematical model, in the form of an optimization problem, is built by modeling the mobile charging station as a queuing process, the goal of the problem being to place a minimum number of temporary service centers (which may have one or more mobile charging stations) to minimize operating costs and the charger capacity of the mobile charging station so that the service offered is efficient. The temporary locations obtained are in areas with no or few fixed charging stations, making the mobile station infrastructure complementary to the fixed charging station infrastructure. The temporary location operational mode, compared to current moving operational mode, is more efficient, having a small miss ratio, short mean response time and short mean queuing time.

Location Optimization of Electric Vehicle Mobile Charging Stations Considering Multi-Period Stochastic User Equilibrium

This study researches the dynamical location optimization problem of a mobile charging station (MCS) powered by a LiFePO 4 battery to meet charging demand of electric vehicles (EVs). In city suburbs, a large public charging tower is deployed to provide recharging services for MCS. The EV’s driver can reserve a real-time off-street charging service on the MCS through a vehicular communication network. This study formulates a multi-period nonlinear flow-refueling location model (MNFRLM) to optimize the location of the MCS based on a network designed by Nguyen and Dupuis (1984). The study transforms the MNFRLM model into a linear integer programming model using a linearization algorithm, and obtains global solution via the NEOS cloud CPLEX solver. Numerical experiments are presented to demonstrate the model and its solution algorithm.

IEEE Smart Village Launches SunBlazer IV and Smart Portable Battery Kits: Empowering Remote Communities

IEEE Smart Village (ISV) developed the first-generation SunBlazer in 2010 as a mobile charging station for portable battery kits (PBKs). These kits enabled people in remote villages to provide power for basic lighting, cell-phone charging, and small appliances. Now, 10 years and three design iterations later, ISV demonstrates its continuing penchant for innovation with the SunBlazer IV and Smart PBK technology, developed as more versatile options for the on-going mission to empower off-grid communities around the world. This fourth-generation equipment is lightweight, modular, rugged, easy to transport and install, and adaptable to a multitude of uses. The kit includes a controller and battery charger in a selfcontained shipping container, which, along with the simplified expandable solar panel with an A-frame design, makes for easier setup in remote areas. The modular design of the SunBlazer IV (Figure 1) allows users to raise the peak power from 1.5 kWp, the peak power of a threesolar-panel base system, to up to 3.7 kWp by extending the A-frame solar panel array. Larger arrays can be created by cascading systems together.

Algorithms and experiments on routing of unmanned aerial vehicles with mobile recharging stations

AbstractWe study the problem of planning a tour for an energy‐limited Unmanned Aerial Vehicle (UAV) to visit a set of sites in the least amount of time. We envision scenarios where the UAV can be recharged at a site or along an edge either by landing on stationary recharging stations or on Unmanned Ground Vehicles (UGVs) acting as mobile recharging stations. This leads to a new variant of the Traveling Salesperson Problem (TSP) with mobile recharging stations. We present an algorithm that finds not only the order in which to visit the sites but also when and where to land on the charging stations to recharge. Our algorithm plans tours for the UGVs as well as determines the best locations to place stationary charging stations. We study three variants for charging: Multiple stationary charging stations, single mobile charging station, and multiple mobile charging stations. As the problems we study are nondeterministic polynomial time (NP)‐Hard, we present a practical solution using Generalized TSP that finds the optimal solution that minimizes the total time, subject to the discretization of battery levels. If the UGVs are slower than the UAVs, then the algorithm also finds the minimum number of UGVs required to support the UAV mission such that the UAV is not required to wait for the UGV. Our simulation results show that the running time is acceptable for reasonably sized instances in practice. We evaluate the performance of our algorithm through simulations and proof‐of‐concept field experiments with a fully autonomous system of one UAV and UGV.

Learning-Based Energy-Efficient Data Collection by Unmanned Vehicles in Smart Cities

Mobile crowdsourcing (MCS) is now an important source of information for smart cities, especially with the help of unmanned aerial vehicles (UAVs) and driverless cars. They are equipped with different kinds of high-precision sensors, and can be scheduled/controlled completely during data collection, which will make MCS system more robust. However, they are limited to energy constraint, especially for long-term, long-distance sensing tasks, and cities are almost too crowded to set stationary charging station. Towards this end, in this paper we propose to leverage emerging deep reinforcement learning (DRL) techniques for enabling model-free unmanned vehicles control, and present a novel and highly effective control framework, called “DRL-RVC.” It utilizes the powerful convolutional neural network for feature extraction of the necessary information (including sample distribution, traffic flow, etc.), then makes decisions under the guidance of the deep Q network. That is, UAVs will cruise in the city without control and collect most required data in the sensing region, while mobile unmanned charging station will reach the charging point in the shortest possible time. Finally, we validate and evaluate the proposed framework via extensive simulations based on a real dataset in Rome. Extensive simulation results well justify the effectiveness and robustness of our approach.