Battery Capacity Constraints Research Articles

Prize-collecting Electric Vehicle routing model for parcel delivery problem

Electric Vehicle (EV)-based parcel delivery is getting much more popular these days due to EVs being eco-friendly and sustainable. One well-known problem in the literature to study last-mile delivery via EVs is the Electric Vehicle Routing Problem (EVRP). One limitation of EVRP is that it assumes all customers must be visited directly by EVs. This forces companies to purchase or utilize additional EVs, increasing operational costs for EV utilization and routing. To overcome this limitation, the present work introduces the Prize-collecting EVRP with Time Windows (PC-EVRP-TW), utilizing multiple EVs for the home-delivery service of high-priority customers (customers with high loyalty or premium memberships), leaving the rest for the next day or outsourcing to a third-party shipper. It also ensures a minimum number of served demands to maintain service quality. PC-EVRP-TW is modeled by mixed-integer linear programming and solved by a problem-specific hybrid metaheuristic aiming to minimize EVs’ route and usage cost and maximize collected profits (prizes) while satisfying EVs’ load and battery capacity, task completion, and time window constraints. Analyses were performed on the optimal solution of PC-EVRP-TW. The results show that the company could achieve significant route cost savings, up to 22% compared to EVRP-TW. Additionally, remarkable reductions in EV usage costs, 6%, 16%, and 18%, were observed for various-size instances of PC-EVRP-TW. These findings highlight the potential for practitioners and managers to realize substantial cost savings and optimize EV usage by selectively serving a prioritized subset of customers daily, particularly when facing high utilization costs and the inability to serve all customers directly.

Finite-horizon energy allocation scheme in energy harvesting-based linear wireless sensor network

Linear wireless sensor networks (LWSNs) are a specialized topology of wireless sensor networks (WSNs) widely used for environmental monitoring. Traditional WSNs rely on batteries for energy supply, limiting their performance due to battery capacity constraints, while renewable energy harvesting technology is an effective approach to alleviating the battery capacity bottleneck. However, the stochastic nature of renewable energy makes designing an efficient energy management scheme for network performance improvement a compelling research problem. In this paper, we investigate the problem of maximizing throughput over a finite-horizon time period for an energy harvesting-based linear wireless sensor network (EH-LWSN). The solution to the original problem is very complex, and this complexity mainly arises from two factors. First, the optimal energy allocation scheme has temporal coupling, i.e., the current optimal strategy relies on the energy harvested in the future. Second, the optimal energy allocation scheme has spatial coupling, i.e., the current optimal strategy of any node relies on the available energy of other nodes in the network. To address these challenges, we propose an iterative energy allocation algorithm for EH-LWSN. Firstly, we theoretically prove the optimality of the algorithm and analyze the time complexity of the algorithm. Next, we design the corresponding distributed version and consider the case of estimating the energy harvest. Finally, through experiments using a real-world renewable energy dataset, the results show that the proposed algorithm outperforms the other two heuristics energy allocation schemes in terms of network throughput.

A Novel Design Strategy for Cantilever Based MEMS Piezoelectric Vibration Energy Harvesters: FEM Parametric Analysis and Modeling

Vibration-based energy-harvesting (VEH) technology is a viable alternative power source that addresses the issue of battery capacity constraints in portable electronic devices. A novel design strategy for developing a piezoelectric vibrational energy harvester (PVEH) based on a microelectromechanical system (MEMS) is proposed in this paper. Through FEM simulation and analysis, a correlation between the proof mass coverage (PMC) and piezoelectric coverage (PEC) for energy harvesters with different beam geometries is developed. The results show that the output power and resonant frequencies of the energy harvester are strongly dependent on the amount of PMC, PEC, and beam geometry. The optimum piezoelectric layer length for a PVEH depends on the amount of PMC and geometry of the beam. Moreover, as the PMC increases, the output power is found to be increasing. The structure with 80% PMC provides maximum output power. Also, a trapezoidal beam will give a lower resonant frequency than a rectangular beam. Thus the optimum design methodology for a cantilever-based MEMS PVEH in a given layout is to select the trapezoidal-shaped beam with a PMC of 60% to 80% and piezoelectric layer length equal to the beam length. This method would provide optimised resonant frequency and output power performance. Additionally, an analytical model is developed for the optimised cantilever PVEH based on trapezoidal geometry, and the simulation findings are validated with the analytical results.

Optimal allocation and route design for station-based drone inspection of large-scale facilities

The utilization of drones to conduct inspections on industrial electricity facilities, including large-sized wind turbines and power transmission towers, has recently received significant attention, mainly due to its potential to enhance inspection efficiency and save maintenance costs. Motivated by the advantages of drones for facility inspection, we present a novel station-based drone inspection problem (SDIP) for large-scale facilities. The objective of SDIP is to determine the locations of multiple homogeneous automatic battery swap stations (ABSSs) equipped with drones, assign facility inspection tasks to the ABSSs with operation duration constraints, and design drone inspection routes with battery capacity constraints, such that minimize the sum of fixed ABSS costs and drone travel costs. The SDIP can be regarded as a variant of the location-routing problem, which is NP-hard and difficult to solve optimally. To obtain the optimal solution of SDIP efficiently, we firstly formulate this problem into an arc based formulation and a route based formulation, and then develop a logic-based Benders decomposition (LBBD) algorithm to solve it. The SDIP is decomposed into a master problem (MP) and a set of subproblems (SPs). The MP is solved by a branch-and-cut (BC) procedure. Once a feasible integer solution is found, the linear relaxation of SPs are solved by a stabilized column generation to generate Benders cuts. If the cost of all the SPs’ optimal LP solutions plus the cost of the MP’s solution is less that current best cost, the SPs are exactly solved by a Branch-and-Price (BP) algorithm to generate the logic cuts. The numerical results on five scales of randomly generated instances validate the effectiveness of the LBBD algorithm. Specifically, the LBBD can solve all small- and middle-sized instances, and seven out of ten large-sized instances in 1000 s. Furthermore, we conduct a sensitivity analysis by varying the attributes of ABSSs and drones, and provide valuable managerial insights for large-scale facility inspection.

Cooperative grid frequency control under asymmetric V2G capacity via switched integral reinforcement learning

In recent years, V2G techniques have been successfully implemented to stabilize frequency deviations in the grid caused by intermittent renewable energy sources. However, the battery capacity constraints and real-time dynamic driving demands of electric vehicles (EVs) in V2G systems hinder the coordination between V2G control and power plant frequency control strategies. It leads to the dynamic asymmetry of regulation up (RU) and regulation down (RD) frequency regulation (FR) capacity of V2G system, which can affect the performance of V2G FR. To cope with this problem, this paper proposes a switched integral reinforcement learning (SIRL) scheme for cooperative grid frequency control. The proposed scheme integrates V2G control with the power plant frequency control, which are cooperated with each other to complete FR tasks. A novel switched neural network (NN) structure is constructed for the training process of the SIRL algorithm to solve the problem of dynamic asymmetric FR capacity. A switched V2G utility function and a global cost function are constructed for RU and RD. Two independent subnetworks with the same structure are adopted for RU and RD learning to optimized V2G control parameters respectively under dynamic asymmetric output constraints. Two case studies of the IEEE 14-bus test system demonstrate the effectiveness and advantages of the proposed scheme.

SRP-HEE: A Modified Stateless Routing Protocol based on Homomorphic Energy based Encryption for Wireless Sensor Network

Due to the wireless nature, the sensors node data are prone to location privacy of source and classification of the packet by unauthorized parties. Data encryption is one of the most effective ways to thwart unauthorized access to the data and trace information. Traditional wireless network security solutions are not viable for WSNs In this paper, a novel distributed forward aware factor based heuristics towards generating greedy routing using stateless routing is SRP-HEE for wireless sensor network. The model employs the homomorphic Energy based encryption technique. Energy based Encryption model is devoted as homomorphic mechanism due to their less computational complexity. Additionally, privacy constraint becoming a critical issue in the wireless sensor networks (WSNs) because sensor nodes are generally prone to attacks which deplete energy quickly as it is exposed to mobile sink frequently for data transmission. Through inclusion of the Forward aware factor on the Greedy routing strategies, it is possible to eliminate the attacking node which is depleting the energy of the source node. Heuristic conditions are used for optimizing the sampling rate and battery level for tackling the battery capacity constraints of the wireless sensor nodes. The Node characteristics of the propagating node have been analysed utilizing kalman filter and linear regression. The cooperative caching of the network information will enable to handle the fault condition by changing the privacy level of the network. The Simulation results demonstrate that SRP-HEE model outperforms existing technique on basis of Latency, Packet Delivery Ratio, Network Overhead, and Energy Utilization of nodes.

A Novel Charging Station on Overhead Power Lines for Autonomous Unmanned Drones

Innovative drone-based technologies provide novel techniques to guarantee the safety and quality of power supply and to perform these tasks more efficiently. Electric multirotor drones, which are at the forefront of technology, face significant flight time limitations due to battery capacity and weight constraints that limit their autonomous operation. This paper presents a novel drone charging station that harvests energy from the magnetic field present in power lines to charge the drone’s battery. This approach relies on a charging station that is easy to install by the drone on an overhead AC power line without modifying the electrical infrastructure. This paper analyses the inductive coupling between the energy harvester and the power line, electrical protection, the power electronics required for maximum power point tracking and the mechanical design of the charging station. A drone that perches on a cable, an end effector for installation procedures and the charging maneuver are described, along with discussion of the robotic and electrical tests performed in a relevant environment. Finally, a lightweight drone charging station capable of harvesting 145 W of power from a 600 A line current is reported.

The multi-depot pickup and delivery problem with capacitated electric vehicles, transfers, and time windows

The Pickup and Delivery Problem has received increasing attention as a result of the recent growth of third-party delivery companies, and electric vehicles (EVs) are becoming a preferable choice for such large delivery systems due to their environmental benefits. The EVs, however, have limited energy capacity; thus, intra-route facilities are required to recharge them. These facilities can also be visited to transfer requests to other vehicles. In this study, we introduce a novel pickup and delivery problem with electric vehicles and transfers. The traditional constraint that each request should be handled by a single vehicle, is relaxed in this problem with transfers, and additionally, we incorporate other practical considerations such as multi-depots, time-windows, and EVs’ battery and carrying capacity constraints. We develop a mixed-integer linear programming model encompassing all these constraints. To address the computational difficulty of the problem, we propose a hybrid heuristic combining Simulated Annealing (SA) and Large Neighborhood Search (LNS). Experimental results reveal that for small instances where CPLEX can find optimal solutions, our heuristic finds them about 90% faster.

The wireless network jamming problem subject to protocol interference using directional antennas and with battery capacity constraints

Wireless networks support the operation and maintenance of a variety of critical infrastructure, and keeping these networks functional in the face of adversarial adversity is a paramount concern of infrastructure managers. Supporting these networks’ continued operability requires a robust understanding of wireless-network functionality, including of the ways in which adversaries may seek to jam such networks using recently developed capabilities. However, past work on wireless network jamming subject to protocol interference has focused on using omnidirectional antennas for the target and the jamming attack nodes and has not considered battery-capacity impacts on the success of these jamming efforts. Based on a field test of an ad hoc network performed by Ramanathan et al. (2005) in which the authors found that directional antennas offer an “order-of-magnitude improvement in the capacity and connectivity of an ad hoc network,” the work in this field should be extended to include directional antennas. By incorporating directional antennas, analysts may more realistically model antennas present in everyday use. In addition, battery capacity of the wireless network nodes can impact the effectiveness of a jamming attack and should be considered. By considering battery capacity, researchers are sure to take into account real-world scenarios in which energy limitations might affect actual network performance. The mathematical model discussed in this paper demonstrates the way in which network jamming is affected by directional antennas, battery capacity, and node density to determine how these factors would impact a robust jamming attack. Particularly noteworthy results include the finding that high battery capacity can offer as much as half an order of magnitude of improvement in data transmission over lower battery capacity in certain cases. These results show that the model could be used to aid decision makers in understanding how to design a network that is robust against jamming attacks.

Synergistic path planning for ship-deployed multiple UAVs to monitor vessel pollution in ports

Traditionally, vessel air emissions are monitored onboard vessels or at fixed points at sea. These methods are not cost-effective for implementing emission control laws that address air pollution monitoring of vessels travelling over a large body of water . Unmanned aerial vehicles (UAVs) equipped with pollution monitoring sensors are becoming a research focus. However, due to battery capacity constraints, the monitoring scope of UAVs is still not optimal. Thus, using a ship (such as a patrol ship) as a UAV mobile supply base can overcome battery limitations and increase monitoring coverage. This paper investigates the joint routing and scheduling problem of ship-deployed multiple UAVs (SDMUs) for the monitoring of pollution from vessels. The artificial bee colony (ABC) algorithm based on simulated annealing is employed to minimize the total monitoring time. The model and solution algorithm are verified by real-time dynamic vessel data from Tianjin Port.

Protocol-based control for nonlinear systems with environment-dependent energy harvesting sensors: An average dwell-time method

In this paper, a protocol-based controller is designed for nonlinear systems with multiple sensors, which are powered by environment-dependent energy harvesting (EDEH) devices. The Round–Robin (RR) protocol is adopted to coordinate the data transmission of sensors. The protocol-based transmission can be realized only when the energy harvested by EDEH devices is sufficient. The aim of this paper is to design the protocol-based controller to ensure the stochastic finite-time boundedness with EDEH and RR protocol. Firstly, modeling the EDEH by a switching sequence with varying sojourn probabilities, assuming a finite battery capacity constraint, and associating protocol-based transmission with a given energy cost, we propose a new recursive model to depict the dynamic of energy levels for each sensor. Then, combining with the stochastic analysis method and the dynamic of energy levels, the explicit expressions of the controller for each environment with average dwell time (ADT) are obtained. Finally, an example is provided to demonstrate the effectiveness of the designed controllers.

Resource Allocation Optimization Based on Energy Efficiency in Green Cloud Radio Access Network

Compared with traditional cellular networks, cloud wireless access network (C-RAN) can save more energy consumption because it centrally processes the signals in the baseband unit (BBU) pool served by the cloud platform. Aiming at the limited fronthaul capacity of the fronthaul link caused by massive data and the huge energy consumption caused by the explosive growth of mobile traffic, an energy-saving resource management scheme that is powered by a mixture of on-grid and green energy is proposed in this paper. Specifically, a new network energy efficiency (EE) model is constructed. The RRH with hybrid energy to supply power of the model in response to green communication and the model can also realize two functions: information decoding and wireless charging at the same time. The total power consumption including remote radio head (RRH), fronthaul link, BBU pool, and wireless charging equipment of user equipment is considered. Based on network energy efficiency model, in addition to considering the user’s quality of service (QoS), data rate requirements, transmission power of RRHs, and maximum battery capacity constraints, the constraints of fronthaul link capacity constraints also are considered in this paper. In order to solve the proposed optimization problem, a joint strategy of power allocation, resource block (RB) allocation, and power allocation ratio adjustment is proposed in this paper. However, the objective function of energy efficiency optimization problem is nonconvex and cannot be solved directly. Therefore, an equivalent convex feasibility problem is reconstructed, and the optimal solution is obtained by Lagrange dual decomposition method. Simulation results show that the proposed resource allocation scheme can provide higher energy efficiency for the network.

Optimal vehicle-to-grid and grid-to-vehicle scheduling strategy with uncertainty management using improved marine predator algorithm

This paper presents a new solution to the problem of scheduling electric vehicles over a period of time. The primary objective is to minimize the total cost of the electricity price for charging/discharging by considering the battery capacity, C-rating, and other physical constraints. The battery life is improved by minimizing the charging/discharging cycles based on the minimum allowable State-of-Charge (SoC) deviation limit. A new Improved Marine Predator Algorithm (IMPA) is proposed by employing an opposition-based learning scheme to a recent marine predator algorithm to solve the proposed charge/discharge scheduling model, and the performance is compared with other state-of-the-art algorithms. Uncertainties in the end SoC mismatch and electricity price are considered to reiterate the scheduling plan. The statistical test results show that the proposed IMPA is better among other algorithms with the rank value of 2.33 under normal conditions and 1.667 with uncertainties at different intervals.

Offline and Online UAV-Enabled Data Collection in Time-Constrained IoT Networks

Recently, unmanned aerial vehicle (UAV) technology is endorsed to enable applications in domains such as Internet of Things (IoT), wireless sensor networks, and cellular networks. Particularly, time-sensitive and energy-limited IoT networks located in hard-to-reach areas require efficient/cost-effective data collection solution. To address this matter, we consider a multi-UAV enabled IoT network, where several UAVs collect data from time-constrained sensor nodes (SNs). In our framework, SNs are managed by cluster heads (CHs), then UAVs collect data from them. We formulate the problem of minimizing system’s deployment costs and operating energy to collect data within deadlines, subject to communication, UAVs mission time, and battery capacity constraints. To solve it, we propose a two-step approach. In the first step, an efficient K-means based method groups SNs and deploys CHs. Then, UAV-based offline and online data collection methods are proposed. In the offline setting where the system’s status is known beforehand, UAV paths are determined using near-optimal meta-heuristics. In simulations, the nearest-neighbor and Tabu search provided best offline performances, conditioned on the system’s parameters. In the online setting where no system information is available, deep reinforcement learning (DRL) based approaches are proposed. Results demonstrate the superiority of the actor-critic solution.

Smart Healthcare: RL-Based Task Offloading Scheme for Edge-Enable Sensor Networks

With the wide application of Internet-of-Medical-Things (IoMTs) or sensor nodes which equipped with sensors. These networked sensors are used to gather enormous data from different smart healthcare applications, and this collected data process for making appropriate decisions. Edge computing is an efficient platform that provides computational resources to collect sensor data. In the meantime, intelligent and accurate resource management by Artificial Intelligence (AI) has become the center of attention, especially in healthcare systems. With the help of AI, IoMT based healthcare devices will remarkably enhance the computational speed and range. But the challenging issue in these energy-hungry, short battery life, and delay intolerant portable devices is inappropriate and inefficient classical trends of fair resource allotment. Thus, this paper proposes Computation Offloading using Reinforcement Learning (CORL) scheme to minimize latency and energy consumption. We first formulate the problem as a combined latency and energy cost minimization problem, satisfying the lack of limited battery capacity and service latency deadline constraints. Moreover, proposed algorithm search optimal available resources node to offload task towards the trade-off between energy and latency. The experimental results show the benefits of the proposed scheme in terms of saving energy, minimizing latency, and maximum utilization of node resources in edge-enabled sensor networks. We are using an iFogSim simulator to validate the proposed scheme under realistic assumptions.

Multiobjective routing optimization of mobile charging vehicles for UAV power supply guarantees

A novel power supply guarantee model based on mobile charging vehicles (MCVs) is developed for solving the battery capacity constraint problem of unmanned aerial vehicles (UAVs) in performing long-term and large-scale missions. In this model, the charging vehicles travel to charging points located under the routes of the UAVs to be charged wirelessly, maximizing the assurance of completing reconnaissance missions. This paper focuses on the scheduling and routing of charging vehicles and achieves multisystem cooperation by constructing two submodels, namely, a UAV reconnaissance routing model and an MCV charging routing model. Furthermore, an improved coevolutionary framework for constrained multiobjective optimization problems with a generalized opposition-based learning strategy (OL-CCMO) is proposed to optimize the two objectives of power supply guarantee investment cost and reconnaissance mission time window deviation. Three scenarios of UAV reconnaissance missions with different levels of complexity are built in the case studies, in which the charging vehicle routes are optimized with multiple algorithms, demonstrating the superiority and applicability of the proposed algorithm by comparison.

Multi-agent reinforcement learning for cost-aware collaborative task execution in energy-harvesting D2D networks

In device-to-device (D2D) networks, multiple resource-limited mobile devices cooperate with one another to execute computation tasks. As the battery capacity of mobile devices is limited, the computation tasks running on the mobile devices will terminate once the battery is dead. In order to achieve sustainable computation, energy-harvesting technology has been introduced into D2D networks. At present, how to make multiple energy harvesting mobile devices work collaboratively to minimize the long-term system cost for task execution under limited computing, network and battery capacity constraint is a challenging issue. To deal with such a challenge, in this paper, we design a multi-agent deep deterministic policy gradient (MADDPG) based cost-aware collaborative task-execution (CACTE) scheme in energy harvesting D2D (EH-D2D) networks. To validate the CACTE scheme's performance, we conducted extensive experiments to compare the CACTE scheme with four baseline algorithms, including Local, Random, ECLB (Energy Capacity Load Balance) and CCLB (Computing Capacity Load Balance). Experiments were accompanied by various system parameters, such as the mobile device's battery capacity, task workload, the bandwidth and so on. The experimental results show that the CACTE scheme can make multiple mobile devices cooperate effectively with one another to execute many more tasks and achieve a higher long-term reward, including lower task latency and fewer dropped tasks.

Minimizing capacity of Electric Vehicle Battery using Bottleneck Traveling Salesman Problem

Electrical vehicle technology has now developed. Various public transportation can start using electric power sources. However, there are battery capacity constraints. When the distance is too far, the battery capacity is not sufficient to provide the required power. In this paper, we will explain how the bottleneck travel salesman problem is applied to minimize battery capacity requirements. So that vehicles can choose the right route by minimizing the maximum distance between destinations. The optimization process is carried out with the Brute Force algorithm and python programming language.

Energy‐Spectral Efficiency Optimization in Wireless Underground Sensor Networks Using Salp Swarm Algorithm

Achieving high data rate transmission is critically constrained by green communication metrics in Wireless Sensor Networks (WSNs). A unified metric ensuring a successful compromise between the energy efficiency (EE) and the spectral efficiency (SE) is, then, an interesting design criterion in such systems. In this paper, we focus on EE‐SE tradeoff optimization in Wireless Underground Sensor Networks (WUSNs) where signals penetrate through a challenging lossy soil medium and nodes’ power supply is critical. Underground sensor nodes gather and send sensory information to underground relay nodes which amplify‐and‐retransmit received signals to an aboveground sink node. We propose to optimize source and relay powers used for each packet transmission using an efficient recent metaheuristic optimization algorithm called Salp Swarm Algorithm (SSA). Thus, the optimal source and relay transmission powers, which maximize the EE‐SE tradeoff under the maximum allowed transmission powers and the initial battery capacity constraints, are obtained. Further, we study the case where the underground medium properties are dynamic and change from a transmission to another. For this situation, we propose to allocate different maximum node powers according to the soil medium conditions. Simulation results prove that our proposed optimization achieves a significant EE‐SE tradeoff and prolongs the network’s lifetime compared to the fixed allocation node power scheme. Additional gain is obtained in case of dynamic medium conditions.

Delivery drone route planning over a battery swapping network

Many enterprises invest on drone delivery research and development to drop off packages at consumers’ doorsteps in a matter of minutes. We study delivery drone route planning over a battery swapping network allowing farther reach by penetrating current battery capacity constraints. A mixed-integer nonlinear programming model is created to plan efficient drone routing over the swapping machines by minimizing the delivery lead time. We develop an exact solution method, evaluate its performance, and compare it with a straightforward nonlinear solver application. A case study highlights the applicability of the model. Data and source code to the solver are publicly shared.