A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms

Electric vehicle adoption is a trend in many countries, and the demand for charging station infrastructure is at a rapid pace. The placement of charging stations is the key research topic of many researchers, but charging scheduling is also a problem that is going to rise in the near future. The proper charger utilization, maintaining coordination between charging stations, and satisfying users' demands are some of the key challenges. The traffic pattern is uncertain, coordination of distances between charging stations and users is done by Euclidean distance. The traffic-aware fair charging scheduling (TAFCS) strategy is proposed, which will have a balance on charger utilization and user prioritization, and keep the fairness by equal distribution of electric vehicles among all the charging stations having a centralized charging system monitored by an aggregator. The distribution of the traffic pattern of electric vehicles is performed by Monte Carlo simulation. The proposed system is tested on the IEEE 33 bus standard system using the predefined voltage limits of each bus and limiting power loss to lessen its burden. The discharging process of 50 electric vehicles (V2G) is performed by optimal placement by obtaining the weakest buses, which makes it an intelligent distribution system. This proposed charging framework is validated on MATLAB R2020a.

Optimal placement of Charging stations (CSs) and infrastructure planning are one of the most critical challenges that face the Electric Vehicles (EV) industry nowadays. A variety of approaches have been proposed to address the problem of demand uncertainty versus the optimal number of CSs required to build the EV infrastructure. In this paper, a Markov-chain network model is designed to study the estimated demand on a CS by using the birth and death process model. An investigation on the desired number of electric sockets in each CS and the average number of electric vehicles in both queue and waiting times is presented. Furthermore, a CS allocation algorithm based on the Markov-chain model is proposed. Grey Wolf Optimization (GWO) algorithm is used to select the best CS locations with the objective of maximizing the net profit under both budget and routing constraints. Additionally, the model was applied to Washington D.C. transportation network. Experimental results have shown that to achieve the highest net profit, Level 2 chargers need to be installed in low demand areas of infrastructure implementation. On the other hand, Level 3 chargers attain higher net profit when the number of EVs increases in the transportation network or/and in locations with high charging demands.

SummaryWith the advent and popularity of electric vehicles (EVs), the intelligent transportation system has adopted them as an alternative to fossil fuel or gasoline vehicles owing to their benefits of reduced greenhouse gas emissions. However, it becomes critical to schedule the vast EVs at the charging station (CS) efficiently while maintaining security, privacy, and optimality during the energy trading. Therefore, we propose a blockchain and zero‐sum game theory‐based energy trading scheme, that is, DEVS, for optimal and secure EV charging at the CS. We incorporate the Interplanetary File System with the blockchain to ameliorate the cost‐efficiency and reliability of EV energy trading. Furthermore, we implement the zero‐sum game theory between players EV and CS to yield an optimal payoff based on pure and mixed strategies during the energy trading procedure. Moreover, we deploy and execute the smart contract (SC) of the blockchain‐based proposed DEVS scheme in the Remix integrated development environment to secure EV energy trading transactions. Additionally, the security of the proposed DEVS scheme is checked and analyzed using a fuzzing‐based Echidna tool for protected energy trading. Finally, the performance evaluation of the proposed DEVS scheme is analyzed using various standard metrics such as profit comparison based on strategies, energy price comparison, CS profit comparison for pure strategy (based on arrival time), convergence comparison, and profit (EV and CS) at saddle point and mixed strategy to avail preserved and optimal energy trading for EVs.

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

In view of the existing problems that multiple vehicles interaction in the selection of fast charging stations for electric vehicles (EVs) and the equalizing the service capability by multiple stations game in station-EVs interaction, a dynamic response strategy of fast charging station-EVs considering interaction of multiple vehicles is proposed. According to this, the charging scheme of EVs and the dynamic service fee of charging stations are decided. Firstly, the charging guidance framework of station-EVs interaction is proposed to describe the information flow relationship for vehicle, station, road and intelligent transportation system (ITS). Secondly, in order to meet the diversified needs of car owners in charging selection, a charging navigation model is established. Considering the impact of dynamic path travel time, a dynamic path selection model of urban road network is established based on the road segment transmission model. Thirdly, in order to accurately analyze the interaction process between vehicles, a charging decision-making method is proposed considering the dynamic evolution of EVs, which reflects the station selection probability of different positions during driving. Fourthly, according to the queuing time of the charging station, the service fee of the charging station is dynamically adjusted to optimize the service capacity of the charging station, and the multi-agent stackelberg game model is established by combining the charging station selection of EVs with the dynamic service fee of charging station. Finally, Sioux Falls urban road network system is used as an example to analyze the path selection, dynamic decision of charging station selection and service fee, and station-EVs interaction strategy. The results show that this method improves the efficiency of electric vehicle charging station searching, guides EVs in the road network to charge orderly, balances the charging load between charging stations and optimizes the service capacity of charging station reasonably.

Electric vehicles (EVs) are becoming a popular mode of transportation. However, one of the main concerns for EV and CNG owners is the availability and accessibility of charging stations. In this project, we present the development and implementation of an EV and CNG charging station finder web application. The app uses the Google Maps API to display the location of nearby pay stations and provide details such as connection type, availability and price. The app also allows users to filter charging and CNG stations according to their preferences and report charging stations as false or unserviceable. User testing is done to evaluate the usability and performance of the application. The results show that the app is easy to use and provides accurate and up-to date information on payment facilities In this system, the User is able to manage All his EVs inside the app plus he has search/book as lot in advance in the charging station. The User can also search an EV station based on nearby, city or kilometers. The System also provides a Roadmap if you enter source & destination with the charging stations on the way according to the kilometers entered. Admin will manage all the stations and slots. The need for developing infrastructure such as charging stations is undeniable. This EV Charging Station web application has been developed to help EV drivers locate available charging stations near them. After locating a charging station, users can also book a slot at the station to charge their vehicle. EV owners can also use this system to plan their trips more efficiently. Users simply need to specify the source and destination. Based on these two parameters, this system prepares a roadmap with all available charging stations along the journey.

Current research about the application of large scale electric vehicles (EVs) is carried out in two fields: (1) From the aspect of the traffic system, charging navigation technologies were proposed, to improve traffic efficiency and charging convenience of the EV driver. (2) From the aspect of the distribution system, the “smart” charging strategies were developed to optimize charging profile, where power loss of distribution system, voltage limits and load variance were taken into account. However, few studies focused on simultaneous improvement of EV owner’s convenience, traffic system performance, charging station performance and distribution system performance after the application of large scale EVs. In this paper, a multi-objective function considering the performance indices of traffic system and distribution system including road travel speed, traffic flow, charging waiting time, power loss of distribution system and voltage of distribution node is developed to schedule large scale EVs’ charging behaviour and obtain optimal performance of the whole system. Constraints including load capacity of charging stations, charging requirements and endurance mileage of EVs are overall considered, and a method for determining weights of the multi-objective function is discussed. A simulation system is built for verifying the effectiveness of proposed strategy. Simulation results shows that, compared with the usual charging scheduling strategy, average heavy congestion ratio of the district around the charging station in the evening rush hours is reduced from 0.52 to 0.48, the percentage of EVs waiting for charging is reduced from 7.5% to 0.5%, the maximal power loss rate of distribution system is decreased by 3.5%, and the maximal voltage deviation of distribution system is decreased by 3.4% due to the proposed strategy.

An agent-based negotiation scheme for the distribution of electric vehicles across a set of charging stations

Recently, electric vehicles (EV) have gained much attention as a potential enabling technology to support CO2 emissions reduction targets. Relative to their internal combustion vehicle counterparts, EVs consume less energy per unit distance, and add the benefit of not emitting any carbon dioxide in operation and instead shift their emissions to the existing local fleet of power generation. However, the true success of EVs depends on their successful integration with the supporting infrastructure systems. Building upon the recently published methodology for the same purpose, this paper presents a “systems-of-systems” case study assessing the impacts of EVs on these three systems in the context of Abu Dhabi. For the physical transportation system, a microscopic discrete-time traffic operations simulator is used to predict the kinematic state of the EV fleet over the duration of one day. For the impact on the intelligent transportation system (ITS), the integration of EVs into Abu Dhabi is studied using a multi-domain matrix (MDM) of the Abu Dhabi Department of Transportation ITS. Finally, for the impact on the electric power system, the EV traffic flow patterns from the CMS are used to calculate the timing and magnitude of charging loads. The paper concludes with the need for an intelligent transportation-energy system (ITES) which would coordinate traffic and energy management functionality.

The integrated optimization of charging station (CS) placement and distributed generation (DG) placement in distribution networks has been examined in this work. The aim is to improve system performance while incorporating the effects of load models and traffic congestion. Power distribution systems have become more complex due to the widespread adoption of electric vehicles (EVs) and the increasing penetration of DGs. To ensure power system stability, efficient resource utilization is essential. This paper proposes an integrated optimization model that combines DG allocation and CS placement using a Quokka Swarm Optimization (QSO) considering both traffic congestion and dynamic load profiles for realistic modelling. The proposed approach aims to minimize system losses, enhance voltage stability and mitigate the impact of traffic congestion on system performance. Simulations are performed with modified IEEE 69-bus radial distribution system. Proposed method for DG allocation and CS placement using QSO is compared with Grey Wolf Optimization (GWO) for analysis. The results show that optimal integration of DGs and CSs leads to a 48.23% reduction in active power losses (from 224.85 kW to 116.41 kW) and an improvement in the minimum bus voltage from 0.9101 p.u. to 0.9568 p.u. (over 5.15%,) under peak load and high congestion scenarios. These findings confirm that strategic coordination of DG and CS locations significantly enhances grid performance.

Aim. Threats of a non-physical nature have a significant effect on the security of intelligent transportation systems (ITS). They may have the form of unlawful interference in the development and implementation of ITS. The creation of a trusted framework for the development and implementation of ITS is examined as in the case of intelligent water transportation systems (IWTS). Problems. By decree of the President of the Russian Federation, ITS, telecommunications and security of information processing are among the priority areas of scientific and technological development. ITS technologies, as well as those involved in the creation of trusted, secure system and application software are among the most important critical high technologies. The operation of ITS involves wide use of computerised systems that implement the latest information and telecommunication technologies, automated and automatic control technologies, artificial intelligence that can pose security threats. ITS are to be developed and operated in a trusted environment. Methods. The paper used the methodology for ensuring the security of IWTS, development of secure hardware and software platforms for secure automated systems, methods of system analysis, dependability theory, information protection, and law. Results. The paper defines the problem of creating a trusted framework for the development and implementation of IWTS, the applicable terminology is developed. The author examined the effect of IWTS on the security of critical information infrastructure (CII) and national security, developed a model of relationships between the IWTS security domains taking into account threats of physical and non-physical origin. Examples of computer incidents within IWTS that caused consequences at the national and international levels are given. The composition of the IWTS facilities attributed to CII is defined, critical processes implemented by standard CII facilities as part of the IWTS are set forth. The author lists conceptual problems of IWTS security, defines the principles of creating a trusted framework for the development and implementation of IWTS. Conclusion. Ensuring the security of IWTS against modern threats requires solving a number of problems associated with the creation of a trusted framework for the development and implementation of IWTS. For the purpose of improving the timeliness and quality of their solution, the paper proposes an intuitive terminology that reflects the subject area and helps finding a common understanding of the security domain by experts from various industries. IWTS facilities have an effect on CII security and national security in general. That is taken into account in the model of relationships between IWTS security domains, demonstrated using cases of computer incidents within IWTS that caused consequences at the national and international level. Given the above, the paper lists IWTS facilities attributed to CII and sets forth examples of critical processes implemented by standard CII facilities as part of the IWTS. The defined list of conceptual IWTS security problems take into account the growing landscape of IWTS security threats that includes insecure software, hardware and software platforms, software and hardware systems and emerging technologies. When developing the principles for the creation of a trusted IWTS development and implementation framework, the author took into account the best practice of implementing the methodology for creating national secure hardware and software platforms of CII facilities that enables the creation of secure automated systems for various applications that are based on domestically-developed solutions. The examined matters are of a systemic nature, which allows using the findings in the development and implementation of ITS in other modes of transportation.

SummaryIn recent days, electric vehicles (EVs) are seen as a viable transportation alternative by industry people to minimize the greenhouse gas emissions problems of regular fuel vehicles. While EVs' performance is limited due to their restricted battery power, charging wait for service, and high resource costs, they are still a viable option. To enhance the EV's performance, this work introduced an optimized priority‐based charge scheduling with dynamic pricing model. For this, a novel modified tunicate swarm algorithm method, which is the enhanced form of the standard tunicate swarm algorithm, is proposed. When an EV arrives at the charging station, the EV user is permitted to choose the type of charging based on his/her requirements. In general, when charging EVs in public stations or during peak hours, customers must be patient and wait for significant periods of time. The introduced model ignores this negative effect. EV users have also mentioned the issue of controlled charging rates as a key barrier. This is a difficult task, but it is accomplished in this study, which optimizes total charge costs. Finally, the results acquired are discussed under overall cost and energy consumption.

Electric and plug-in hybrid vehicles and their infrastructure in a particular European region

Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. Optimizing the crop cultivation pattern, in order to reduce water consumption, in arid and semi-arid regions such as Iran, due to water scarcity and food intake, is an essential solution for food intakes needs. In this study, new methods based on the election algorithms (EA) and gray wolf optimizer (GWO) algorithms were used to determine the optimal cultivation pattern in Moghan plain during the statistical years of 2007-2016. The objective function in the agricultural sector was based on each product and its yield, net from each product and the cultivar. Then, maximization of the objective function was performed using GWO and EA algorithm. The results of using GWO algorithm in determining the optimal crop pattern in Moghan plain showed that using economic policies such as changing the cultivar pattern, we can obtain a better result compared to EA algorithm in the agricultural sector. In general, the results of GWO algorithm showed that in the Moghan plain with 0.9, 140 billion rials, that is, about 42 % will have economic growth. In sum, the results showed that GWO algorithm with high values of statistical criteria (R2=0.96, RMSE=0.022 and NSE=0.75) has a higher efficiency in optimizing the crop cultivation pattern of Moghan plain, which can be applied to the correct planning for other cultivation areas to be employed.

Electric Vehicles (EVs) have gained significant attention in recent years, mostly owing to the advancements in electric motors and minimal impact on the environment. In the same way, the rapid development of the Internet of Things (IoT) facilitated the interconnection of an increasing number of devices. A significant challenge that EVs have presently is the restricted battery range and a shortage of charging or battery switching facilities. The Battery Management System (BMS) is significant in EVs since it regulates and monitors battery functionality, providing optimal efficiency and prolonging battery lifespan. Integrating IoT and Blockchain (BC) in BMS offers a promising approach to enhance energy efficiency and battery state evaluation. In this research, a BMS model for EVs using IoT, blockchain and machine learning (ML) is developed. The IoT sensors have been equipped to the electric vehicles to gather data such as the level of charging, the distance to be travelled, and the EVs location. For determining the cost of charging, this information is stored and processed by a database, and then it is given as input to the Extreme Gradient Boosting (XGBoost) classifier. After that, the power scheduling based on the Grey Wolf Optimization (GWO) technique processes it to determine the location of the charging station, as well as the time and space of charging that is closest. Finally, this data is stored in blocks to prevent intruders from gaining access to electric vehicles and to ensure that pricing transactions between users and charging stations are carried out in a secure manner. For these secure transactions, a permissioned blockchain with homomorphic encryption (HE) is implemented. The results show that the research work produced an enhanced BMS model with an accuracy rate of 97.36% in detecting close charging station and that it maintains a communication overhead with 35 ms, which was 14% lower than the compared models.