Mobile Charging Stations Research Articles

Auction-Based Charging Scheduling With Deep Learning Framework for Multi-Drone Networks

State-of-the-art drone technologies have severe flight time limitations due to weight constraints, which inevitably lead to a relatively small amount of available energy. Therefore, frequent battery replacement or recharging is necessary in applications such as delivery, exploration, or support to the wireless infrastructure. Mobile charging stations (i.e., mobile stations with charging equipment) for outdoor ad-hoc battery charging is one of the feasible solutions to address this issue. However, the ability of these platforms to charge the drones is limited in terms of the number and charging time. This paper designs an auction-based mechanism to control the charging schedule in multi-drone setting. In this paper, charging time slots are auctioned, and their assignment is determined by a bidding process. The main challenge in developing this framework is the lack of prior knowledge on the distribution of the number of drones participating in the auction. Based on optimal second-price-auction, the proposed formulation, then, relies on deep learning algorithms to learn such distribution online. Numerical results from extensive simulations show that the proposed deep learning-based approach provides effective battery charging control in multi-drone scenarios.

Rendezvous Planning for Multiple AUVs With Mobile Charging Stations in Dynamic Currents

Operation of autonomous underwater vehicles (AUVs) in large spatiotemporal missions is currently challenged by onboard energy resources requiring manned support. With current methods, AUVs are programmed to return to a static charging station based on a threshold in their energy level. Although this approach has shown success in extending the operational life, it becomes impractical due to interruption of AUV operation and loss of energy needed to return to charging station. It is also not practical for large networks due to shortage of charging stations. We introduce mobile onsite power delivery, which will fundamentally change the range and duration of underwater operations. This letter presents a mission planning method to generate mobile charger trajectories, given pre-defined working AUV trajectories, considering environmental constraints such as currents and obstacles. The problem is formulated as a multiple generalized traveling salesman problem, which is then transformed into a traveling salesman problem. Energy cost in dynamic currents is integrated with a path planning algorithm using a grid-based environment model. A scheduling strategy extends the problem over multiple charging cycles. Simulation results show that the planning method significantly improves mission success and energy expenditure. Field experiments in Lake Superior using two types of AUVs, an unmanned surface vessel, and a manned support vessel validate the feasibility of the planned trajectories for long-term marine missions.

Contract-based approach to provide electric vehicles with charging service in heterogeneous networks

Recently, mobile charging stations (MCSs) have attracted more attentions compared with fixed charging stations (FCSs). Electric vehicles (EVs) can be easily provided with charging service through MCSs. However, most of existing approaches cannot be properly used to design the optimal pricing strategy for MCSs, leading to the inefficiency of power in MCSs. Thus, it is necessary to study new incentive mechanisms to improve MCSs’ profits. In this paper, we propose a contract-based scheme to maximize MCSs’ profits in the heterogeneous networks. Considering the power trading between EV users and MCSs, we develop the utility function with EV users’ types. Aiming to maximize MCSs’ profits, we formulate this problem as an optimization problem under the complete and incomplete information of EV users, respectively. Through the theoretical analysis, we prove the existence of optimal contract items, which also ensure the feasibility of EV users. Then optimal solutions can be achieved based on our proposed algorithm. Numerical and simulation results validate the effectiveness of our proposal.

Persistent Surveillance With Energy-Constrained UAVs and Mobile Charging Stations

We address the problem of achieving persistent surveillance over an environment by using energy-constrained unmanned aerial vehicles (UAVs), which are supported by unmanned ground vehicles (UGVs) serving as mobile charging stations. Specifically we plan the trajectories of all vehicles and the charging schedule of UAVs to minimize the long-term maximum age, where age is defined as the time between two consecutive visits to regions of interest in a partitioned environment. We introduce a scalable planning strategy based on 1) creating UAV-UGV teams, 2) decomposing the environment into optimal partitions that can be covered by any of the teams in a single fuel cycle, 3) uniformly distributing the teams over a cyclic path traversing those partitions, and 4) having the UAVs in each team cover their current partition and be transported to the next partition while being recharged by the UGV. We show some results related to the safety and performance of the proposed strategy.

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.

Dynamic Charging Optimization for Mobile Charging Stations in Internet of Things

The increasing use of Internet of Things (IoTs) has brought more advantages in supplying power to electric vehicles (EVs). With the help of IoTs, EVs can be charged more easily by mobile charging stations (MCSs) compared with the fixed charging stations (FCSs). However, previous works in the management of power supply in FCSs have not been properly applied in MCSs, e.g., dynamic of EV users’ arrival and variable power supply from MCSs in IoTs. In this paper, we study how to manage MCSs’ supply power in IoTs under the condition that MCSs supply multiple kinds of power. First, considering the randomness of power supply and dynamic of EV users’ arrival, we develop the dynamic framework of power supply and the economic model. Then, aiming to maximize the long-term average profits of MCSs, a stochastic optimization problem is formulated to decide the optimal strategy of power management. Based on the Lyapunov optimization theory, a Lyapunov-based online distributed algorithm is proposed to obtain the optimal solutions. Meanwhile, the performance of our proposed algorithm is analyzed and simulation results validate the effectiveness of our proposal.

Route Optimization of Electric Vehicles Based on Dynamic Wireless Charging

One of the barriers for the adoption of electric vehicles (EVs) is the anxiety around the limited driving range. Recent proposals have explored charging EVs on the move, using dynamic wireless charging which enables power exchange between the vehicle and the grid while the vehicle is moving. In this paper, we focus on the intelligent routing of EVs in need of charging so that they can make most efficient use of the so-called mobile energy disseminators (MEDs) which operate as mobile charging stations. We present a method for routing EVs around MEDs on the road network, which is based on constraint logic programming and optimization using a graph-based shortest path algorithm. The proposed method exploits inter-vehicle communications in order to eco-route electric vehicles. We argue that combining modern communications between vehicles and state of the art technologies on energy transfer, the driving range of EVs can be extended without the need for larger batteries or overtly costly infrastructure. We present extensive simulations in city conditions that show the driving range and consequently the overall travel time of electric vehicles is improved with intelligent routing in the presence of MEDs.

Design and Implementation of Real Time Charging Optimization for Hybrid Electric Vehicles

<p>Electric vehicle (EV) has gained incredible interest from the past two decade as one of the hopeful greenhouse gasses solution. The number of Electric Vehicle (EV) is increasing around the world; hence that making EVs user friendly becomes more important. The main challenge in usage of EV is the charging time required for the batteries used in EV. As a consequence, this subject matter has been researched in many credentials where a wide range of solutions have been proposed. However those solutions are in nature due to the complex hardware structure. To provide an unswerving journey an Android application based charging optimization is proposed. This application is aimed at giving relevant information about the EV’s battery state of charge (SOC), accurate location of the EV, booking of the charging slots using token system and route planner. At emergency situations, an alternative service is provided by mobile charging stations. Route planner indicates the temperature by which prediction of reaching the destination can be done. In addition to that nearest places such as parks, motels are indicated. The estimated time and distance between the electric vehicle and the charging station is calculated by the charging station server according to which the parking lot is allocated. Vehicle to charging station communication is established for the time estimation of charging. This will help the EV users to know about charge status and charging station, which support fast charging method and availability of the station on the go and also when to charge their EV. The Arduino UNO board has been used for the hardware part. The hardware results are confirming the conceptual of the proposed work.</p>