EVMCSDLT: Electric vehicle mobile charging system using distributed ledger technology.

<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>

The global electric vehicle market is increasing. The main motivation for switching to this innovative type of vehicle is, first of all, reducing the impact on the environment, reducing operating costs, the possibility of using new technologies in transport, etc. Of course, the development of electric vehicles, like other industries, has its problems. The paper examines the methodology for determining the optimal options for charging infrastructure for electric vehicles, as the main factor influencing the widespread use of electric cars. The purpose of the study is to determine the optimal option for mobile charging stations (MCS) for the conditions of Tajikistan based on a comparative analysis of key indicators: cost, weight and resource. To conduct a study to determine the optimal option for mobile charging stations, a comparative analysis method was used. For the conditions of Tajikistan, to determine the optimal option for mobile charging stations (MCS), a corresponding comparative analysis was carried out in terms of such indicators as cost, weight, and resource. It was revealed that the MCS based on autonomous generators with an internal combustion engine (ICE) is superior to other options in all respects. However, we should not forget that the greatest harm is caused to the environment by such an MCS, and it requires a constant supply of fuel. The best option is to combine lithium-ion batteries with an autonomous generator and a solar battery or wind power plant. Considering that the multi-station system is often located in one place, at the first stage it was proposed to create a multi-station station with an autonomous generator, lead-acid batteries, or a hybrid version. In the future, it is necessary to gradually switch to the joint use of hybrid installations based on lithium-ion batteries in combination with an internal combustion engine, solar panels, or a wind-electric installation. The practi¬cal significance of the study is to create a stable and cost-effective infrastructure for charging electric vehicles, including based on alternative energy sources, in Tajikistan, which contributes to increased mobility, economic development and environmental sustainability of the region. The development of electric vehicle infrastructure will stimulate the growth of the electric vehicle market, which in turn will contribute to the development of related economic sectors. Similar installa¬tions can be used in other industries, such as tourism, power supply to remote villages, and geological exploration.

In Internet of Electric Vehicles (IoEV), mobile charging stations (MCSs) have been deployed to complement fixed charging stations. Typically, MCSs are assigned to charge the electric vehicles with insufficient electricity which have made charging requests (termed IEVs). Moreover, there are some electric vehicles with insufficient electricity which have not made charging requests (termed quasi-IEVs). If idle MCSs are allowed to actively track quasi-IEVs according to their potential charging demand, then more IEVs could be promptly charged, and thus the charging profits of MCSs could be increased. However, due to the private ownership of electric vehicles, some private information cannot be provided in the potential charging demand of quasi-IEVs (e.g., the destinations and residual electricity), making the potential charging profits of idle MCSs hard to be evaluated, and thereby the proper assignments of idle MCSs are difficult to decide. To this end, we introduce the profit-maximizing heat maps to depict the potential charging demand of quasi-IEVs and evaluate the potential charging profits of idle MCSs. A profit-maximizing heat map remarks the positions around quasi-IEVs and displays them as continuous areas. Specifically, the different shades of colours are used to distinguish the quantities of potential charging profits of idle MCSs, and the sizes of coloured areas are used to indicate the possibility of quasi-IEVs passing through these positions. In this paper, we propose a Profit-Maximizing Assignment Strategy of Idle MCSs (PMASIM) to properly assign the idle MCSs to charge IEVs at selected charging positions, or track some quasi-IEVs according to the profit-maximizing heat maps. Extensive simulations and comparisons demonstrate the superior performance of PMASIM, i.e., with the profit-maximizing heat maps, the charging profits of MCSs are increased, and the proportion of charged IEVs is enhanced as well.

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.

Mobile charging stations (MCSs) can provide electric vehicles (EVs) with better charging services than the fixed charging stations, as the flexible and efficient charging sites can be available. However, how to schedule the tasks from the EVs and optimally place the MCSs becomes a new challenge. Therefore, in this paper we present a novel approach to help EVs$^{'}$ charging with MCSs through heterogeneous networks. Firstly, a novel heterogeneous network model is presented to improve the communication between EVs and MCSs by using macro cells and small cells. Next, a novel model is developed to make optimal decisions for MCSs to schedule the tasks from EVs. Then, a chaotic evolution particle swarm optimization (CEPSO) algorithm is presented to determine the optimal placement of MCSs based on the charging demand and the maintenance cost. Finally, the simulation experiments prove that the proposed approach can outperform the conventional methods.

With The integration of renewable energy sources into smart grids and electric vehicle (EV) charger-sharing networks is essential for achieving the goal of environmental sustainability. However, the uneven distribution of distributed energy trading among EVs, fixed charging stations (FCSs), and mobile charging stations (MCSs) introduces challenges such as inadequate supply at FCSs and prolonged latencies at MCSs. In this paper, we propose a multi-agent deep reinforcement learning (MADRL)-based auction algorithm for energy trading that effectively balances charger supply with energy demand in distributed EV charging markets, while also reducing total charging latency. Specifically, this involves a MADRL-based hierarchical auction that dynamically adapts to real-time conditions, optimizing the balance of supply and demand. During energy trading, each EV, acting as a learning agent, can refine its bidding strategy to participate in various local energy trading markets, thus enhancing both individual utility and global social welfare. Furthermore, we design a cross-chain scheme to securely record and verify transaction results of energy trading in decentralized EV charger-sharing networks to ensure integrity and transparency. Finally, experimental results show that the proposed algorithm significantly outperforms both the second-price and double auctions in increasing global social welfare and reducing total charging latency.

Toward social equity access and mobile charging stations for electric vehicles: A case study in Los Angeles

In the context of increasing global energy scarcity, optimizing electric vehicle (EV) mobile charging stations is critical for promoting sustainable transportation. This study introduces the use Artificial Neural Network (ANN) models, and Support Vector Machine (SVM) enhanced with the Adams optimizer, to address the challenge of efficient EV charging in energy-constrained environments. The models are designed to predict optimal charging station locations and schedules, with the ANN-Adams optimization fine-tuning the model parameters to improve accuracy and performance, while ensuring dynamic adaptation to fluctuating energy availability and demand patterns. This research contributes to the development of intelligent, adaptive systems for EV infrastructure, paving the way for more resilient and energy-efficient urban mobility solutions. The system employs supervised learning, where models were trained and tested on labeled datasets. The performance of the SVM models was compared to that of a Multilayer Perceptron Network (MLPN) with Adams optimization. The results showed that the MLPN with Adams optimization achieved an accuracy of 97.85%, while the SVM model had a prediction accuracy of 81%. The ANN recorded 91.29% accuracy. Both approaches significantly improved classification accuracy, model generalization on testing datasets, and reduced misclassification errors.

Electric vehicle (EV) charging infrastructure development is one of the key aspects of the electrification of transportation systems. However, incorporating EV public charging facilities has practical and financial concerns, especially in developing economies. Recently, mobile public charging stations (MPCS) have been considered an alternate, practical, commercially scalable solution. Present research on MPCS operation algorithms assumes centralized algorithm execution and does not offer any features related to consumer data security. Moreover, they do not exploit the multi-port charging facility of MPCS, which is more economical for scheduling services. In this paper, a comprehensive framework for the decentralized execution of the MPCS operation algorithm for EV charging service using a hybrid cloud-edge server architecture is proposed. The decentralized execution enhances EV data security and reduces load on the cloud server, data transmission, and storage charges. Four vehicle routing algorithms, including exact integer programming (IP), Greedy, Greedy+2Opt heuristic, and meta-heuristic Tabu search algorithms, are applied to test the efficacy of the proposed framework. From the studies, the proposed framework with Greedy+2Opt performed well concerning scalability. Compared to one-to-one MPCS-EV charging, the proposed one-to-many charging framework offered a significant reduction in the transportation cost and the required number of MPCSs for a given energy delivered. Further, for a given MPCS battery capacity and service time window, the framework delivered higher energy compared to one-to-one service model.

Recent technological breakthroughs have allowed unmanned aerial vehicles (UAVs) to be utilized in a broad range of new operations. Among these various applications, herein, we focus on the use of UAVs for search and rescue missions in emergency and postdisaster scenarios. In this context, self-charging technologies for drones create new challenges in the routing of UAVs with charging stations. We present a variant of the vehicle routing problem (VRP) to address the integrated use of UAVs and mobile charging stations and define the VRP with synchronized networks (VRPSN), a new class of VRPs involving the routing of UAVs whose recharge platforms can travel to different locations during an operation. This leads to two networks within the VRP that must be integrated and synchronized. This research develops a mixed-integer linear program model for the VRPSN that considers the use of UAVs and mobile charging stations in a synchronized manner. To overcome the computational limits of the MILP model, this research presents a construct-and-adjust heuristic method integrated with a genetic algorithm. As a numerical example, we test the proposed model on the Córrego do Feijão Mine located in Minas Gerais, Brazil, where a dam recently collapsed, killing many workers. Numerical tests show that the new methodology is an attractive planning method for providing efficient and rapid responses in search and rescue missions.

The number of words should not exceed 350. Energy and transport resilience are global issues of interest to researchers. This paper discusses the increase in electric vehicle charging (EVC) stations in Indonesia and related challenges in addressing climate change. National production of electric vehicles, especially cars increased exponentially throughout 2024. Yet, the ratio of fuel stations (FSs) to EVC stations remains disproportionate. Electric cars run out of battery power on the road due to limited availability of EVC stations, thus necessitating an effective solution. This situation requires addressing innovatively. EV mobile charging stations (MCSs) can overcome the infrastructure transition limitations involved. Solar-powered MCSs could be one such solution. Additionally, MCSs for EVs can potentially serve as multipurpose power stations for disaster mitigation.

This study addresses the deployment and operational optimization of mobile charging stations (MCSs) to meet the growing demand for electric vehicle (EV) charging in underserved multi-family dwelling (MFD) areas. As EV adoption continues to rise, MFD residents face persistent challenges in accessing convenient and reliable charging infrastructure. To bridge this gap, we propose a comprehensive Mixed-Integer Linear Programming (MILP) framework that optimizes MCS fleet operations, including strategic hub selection, fleet sizing, routing, and scheduling. The model seeks to maximize net service benefits by minimizing depot activation and operational costs while ensuring that charging demand is fully satisfied. It incorporates key constraints such as MCS battery dynamics, routing feasibility, energy constraints, and service time windows. A case study in Indianapolis, Indiana, demonstrates the framework's effectiveness in serving high-demand MFD clusters via optimized multi-stop routes originating from a single activated depot, thereby significantly reducing system-wide costs. Compared to a baseline approach involving uncoordinated depot and vehicle assignments, the proposed method achieves as much as 45.1% cost savings while maintaining complete service coverage with fewer vehicles. These results underscore the potential of MCS-based solutions to enhance charging accessibility in infrastructure-constrained urban environments. The proposed framework provides a practical planning tool for municipalities seeking to accelerate EV adoption through flexible, cost-effective, and equitable charging strategies.

We consider persistent (long-horizon) 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. The goal is to periodically visit a set of monitoring points by the UAVs while minimizing the maximum time between the consecutive visits to any of those points. In general, the optimal planning of UAVs and UGVs in such a persistent surveillance scenario is an NP-hard combinatorial optimization problem. Furthermore, the problem also demands a solution strategy that can successfully handle obstacles, especially on the ground, that are unknown a priori in many real-life scenarios. We present a scalable and robust approximate algorithm that is based on 1) forming uniform UAV-UGV teams, 2) decomposing the environment into maximal partitions that can be covered by the UAVs in a single fuel cycle as long as the UAVs are released sufficiently close to the centers of the partitions, 3) maintaining the teams uniformly distributed 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 support our proposed algorithm with some theoretical results and simulations.

The charging transaction data of electric vehicle (EV) users is crucial for studying charging market dynamics and formulating effective policies. However, due to factors such as the privacy of EV users and the complex coupling relationships between charging dealers, existing EV charging transaction datasets are plagued by issues such as incompleteness, significant bias, and a lack of real-time information. To address these issues, a real-time charging transaction dataset has been created, comprising 441,077 charging transactions collected from 13 charging stations in China over a 2-year period. The dataset includes detailed data of EV user charging transaction time, price and charging status, as well as the charging termination reasons and weather data for each charging session. This dataset offers references for identifying EV user behaviors and extracting charging fault factors from multiple aspects, supporting research applications in EV charging facility planning, EV charging and discharging management, and charging economic evaluation.

High penetration of renewable energy in the power grid causes serious frequency deviations. Large-scale integrations of electric vehicles (EVs) in the power grid provide considerable vehicle-to-grid potential for frequency regulation. Existing frequency regulation strategies with aggregated EVs realize accurate power control that relies on complete information interaction between the EV aggregator and individual EVs. However, the data collection for all EV parameters is not applicable due to privacy protection and the limited communication environment. Considering the limited data collection from grid-connected EVs, this paper provides a novel frequency regulation strategy and tends to address the uncertain influence from EV users’ charging requirements, the EV aggregator’s power regulation, and the frequency regulation performance. Firstly, considering the influence of the limited data collection by EVs on the users’ requirement of traveling and regulation preference, a probabilistic evaluation model for the available regulation capacity of the EV aggregator and the probabilistic control method for EVs are developed. Then, a frequency regulation strategy with error correction control and progressive regulation recovery is developed to simultaneously guarantee the system frequency regulation performance and the regulation requirements of EV users. Finally, case studies are carried out to validate the effectiveness of frequency regulation strategy for decreasing the uncertain influence from the limited data collection, ensuring the EV users’ requirements, and improving the system frequency stability.