Electric vehicle charging stations selection in large cities based on improved sparrow search algorithm

Data-driven equitable placement for electric vehicle charging stations: Case study San Francisco

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.

Aiming at the problems of high investment and low efficiency in the planning and construction of electric vehicle (EV) charging stations in cities, an optimization model for site selection and capacity determination of charging stations considering the uncertainty of users’ dynamic charging demands is proposed. Firstly, based on the travel chain theory and the Origin-Destination (OD) matrix, the travel characteristics of EVs are studied, and the spatial and temporal distribution prediction model of EV charging load is established through the dynamic Dijkstra algorithm combined with the Monte Carlo method. Secondly, a site selection model for the charging station is established which takes the minimum annualized cost of the charging station operator and the annualized economic loss of the EV users as the goal. At the same time, the weighted Voronoi diagram and Adaptive Simulated Annealing Particle Swarm Optimization algorithm (ASPSO) are adopted to determine the optimal number/site selection and service scope of charging stations. Finally, an uncertain scenario set is introduced into the capacity determination model to describe the uncertainty of the users’ dynamic charging demands, and the robust optimization theory is utilized to solve the capacity of the charging station. A case study is carried out for the EV charging station planning problem in some urban areas of a northern city, and the validity of the model is verified.

Coverage evaluation of public electric vehicle charging stations in Bangkok, Thailand using location-allocation model

Electric Vehicles (EVs) can effectively mitigate global warming issues while ensuring energy security, when compared with conventional fuel-based vehicles. Therefore, proper planning and development of charging infrastructure are essential to promote EVs. This paper proposes a multi-objective formulation to determine fast charging stations’ optimal placement and sizing on intra-city corridors in a coupled transportation and Electrical Power Distribution Network (EPDN). The proposed formulation does a distance-based mapping between all transportation and EPDN nodes for precisely observing the impact of EV charging on EPDN. The proposed planning simultaneously considers the objectives and constraints of the transportation network and EPDN while satisfying the EV charging requirements. The EV charging demand, utilized in planning, is predicted using the Random Forest technique while considering the day type, hourly weather, and traffic flow that affect the forecasting accuracy. The improved Particle Swarm Optimization with Constriction Factor solves the proposed formulation for a 30-node EPDN coupled with a 25-node transportation network. The analysis of various cases, including varying initial State-Of-Charge of EVs and percentage growth of EVs per year, proves the efficacy and robustness of the proposed work. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The planned deployment of charging infrastructure is critical to facilitate EVs’ adoption and sustainable growth of EV industries. Since, EV charging stations couple transportation network and EPDN, the planning strategy should consider requirements of both networks. For satisfactory driving experience of EV users, the planning should also consider driving range constraints and charging requirements of EV users. Several works on charging infrastructure planning have been carried out. However, several aspects relevant to EV users, transportation networks, EPDN, and determining EV charging demand have been overlooked in existing literature (as evidenced through Table comparisonlitercomparisonliter). These aspects must be considered simultaneously while planning charging stations. Hence, this work proposes a planning strategy for optimal placement and sizing of fast EV charging stations in a metropolitan city connecting multiple suburbs while considering several aspects of transportation networks and EPDNs. The proposed strategy can be utilized by utilities for efficient deployment of charging infrastructure on metropolitan city intra-city corridors. It establishes a distance-based mapping between all transportation and EPDN nodes for observing the impact of EV charging on EPDN. The determination of EV charging demand is a crucial step in the planning, which is determined by forecasting hourly traffic flow while considering several key attributes. The proposed approach achieves the EPDN objectives of reducing power loss, voltage deviation, and total charging stations’ establishment cost, and transportation network objectives of maximising captured traffic flow while satisfying driving range constraints and charging requirements of EVs. Therefore, the focus is to utilize the existing grid infrastructure optimally while achieving the objectives and satisfying constraints of EV users, transportation network, and EPDN. Relevant case studies demonstrate the efficacy and robustness of proposed approach. The uncertainties in EVs’ injections and arrival/ departure times will be considered in our future work.

A particle swarm optimizer-based optimization approach for locating electric vehicles charging stations in smart cities

The crowded urban environment and busy traffic lead to heavy roadside pollutions in high-density cities, thereby causing health damages to city pedestrians. Electric vehicle (EV) is considered as a promising solution to such street-level air pollutions. Currently, in high-density cities, the number of public charging stations is limited, and they are far from enough to form a complete charging network with a high coverage ratio that can provide easy and convenient charging services for EV users. Concerns and worries on being unable to find a charging port when needed become a major hurdle to EV practical applications. Meanwhile, greener and cheaper renewable energy is recommended to replace fossil fuel-based grid energy that is commonly used in existing charging stations. Thus, this study proposes a novel geographic information system (GIS)-assisted optimal design method for renewable-powered EV charging stations in high-density cities. By selecting the optimal locations and optimal number of the renewable-powered charging stations with the considerations of the existing charging stations and renewable potentials, the proposed method is able to minimize the life cycle cost of the charging stations while satisfying a user-defined area coverage ratio. Using Hong Kong as an example, case studies have been conducted to verify the proposed design method. The design method can be used in practice to help high-density cities build their public charging networks with cost-effectiveness, which will promote EV practical applications and thus alleviate the roadside air pollutions in high-density cities.

Geographic Information System-assisted optimal design of renewable powered electric vehicle charging stations in high-density cities

This chapter discusses the available charging systems for electric vehicles (EV) which include battery electric vehicles (BEV) and plugged hybrid electric vehicles (PHEV). These architectures are categorized as common DC bus charging (CDCB) station and common AC bus charging (CACB) station. CACB charging stations are generally used as slow chargers or semi-fast chargers (on-board chargers). CDCB charging stations are used as fast chargers (off-board chargers). These chargers are vital to popularize the electric vehicles (EVs) as a green alternative to the internal combustion engine (ICE) vehicles. Further, this chapter covers the power quality problems related to the grid-connected fast charging stations (FCS), AC-DC converter, control strategies for converters, proposed system of architectures, methodology, system results with comparisons, and finally, a conclusion.

Currently, there is a massive use of electric vehicles in the world. This is due to the ever-increasing importance and severity of the environmental safety issue. Now, there is no more convenient, affordable, popular and at the same time less harmful alternative to road transport. The annual global emissions are more than 25 million tons of pollutants of various origins, which are extremely dangerous for nature in general. On average, they increase by 3% every year. The transport sector accounts for 23% of all greenhouse gas emissions in the world. Electric vehicles can contribute enormously to the reduction in greenhouse gas emissions. In 2025, sales of electric vehicles are expected to increase to 100 000 units. By 2030, the number of electric vehicles will reach 1.5 million units. However, widespread adoption of electric vehicles will cause an increase in electricity consumption. In addition, an appropriate charging infrastructure is required to power electric vehicles. It is worth noting that the market for electric vehicles and charging stations is developing dynamically with an annual growth rate up to 80%. The main barriers to the development of electric vehicles in Russia are their high cost and underdeveloped network of charging stations. The relevance of the problem is confirmed by an order signed by Prime Minister Mikhail Mishustin in March 2022 to allocate about 1.37 billion rubles for the creation of charging infrastructure for electric vehicles in Russia. As for incentives, there are many of them. This is the need to solve the environmental problem in large cities, achieve the goal of reducing CO2 emissions and pursue industrial policy, including the policy of innovative development of the country. Another way of increasing the payback of charging stations is the employment of smart services, which can reduce the cost of technological connection through dynamic power distribution between the main facility and the charging station. There are other, more advanced solutions, which are currently being piloted, such as optimization of investment in network infrastructure, including an operator of a regional public charging infrastructure. This study focuses on the development of a power supply system to connect an electric vehicle charging station to a substation in Novosibirsk. The connected electric vehicle charger, being a non-linear load, can not only increase the load on the power grid infrastructure, but also cause a deterioration in the power quality due to the generation of harmonic distortions. Therefore, special attention is paid to the analysis of the power supply quality during the operation of the charging station by using the model developed in the MATLAB Simulink software for the analysis of harmonics.

The number of electric vehicles (EVs) continues to increase in the automobile market, driven by public policies since they contribute to the global decarbonization of the transportation sector. Still, the main challenge to increasing EV adoption is charging infrastructure. Therefore, the site selection of public EV charging stations should be made very carefully to maximize EV usage and address the population’s range anxiety. Since electricity demand for charging EVs introduces new load shapes, the interrelationship between the location of charging stations and long-term electrical grid planning must be addressed. The selection of the most suitable site involves conflicting criteria, requiring the application of multi-criteria analysis. Thus, a geographic information system-based Multicriteria Decision Analysis (MCDA) approach is applied in this work to address the charging station site selection, where the demographic criteria and energy density are taken into account to formulate an EV increase model. Several methods, including Fuzzy TOPSIS, are applied to validate the selection of suitable sites. In this evaluation, the impact of the EV charging station on the substation capacity is assessed through a high EV penetration scenario. The proposed method is applied in Cuenca, Ecuador. Results show the effectiveness of MCDA in assessing the impact of charging stations on power distribution systems ensuring suitable system operation under substation capacity reserves.

Background:: The global transition to green energy and the rapid development of Electric Vehicle (EV) technology, along with falling component costs, have fueled the growing popularity of electric vehicles. To support the widespread adoption of EVs, an efficient and userfriendly charging infrastructure is crucial. Objective:: This work aims to propose a comprehensive EV charging system that addresses the rising demand for charging stations, streamlines the charging process, and empowers EV drivers with essential information. The primary focus is on an economical and effective booking system, enabling users to locate nearby charging stations and make informed choices about their charging preferences Method:: We suggest developing a EVs Charging Finder App, serving as a central platform for EV users to find nearby charging stations. The app will provide vital details, including ratings, reviews, available time slots, charging duration estimates, and more. Users can also contribute new charging station data, fostering app growth. Additionally, an alert system will notify users when nearby charging slots become available, enhancing convenience for EV drivers. Results:: The EVs Charging Finder App is anticipated to significantly enhance the accessibility and convenience of EV charging. Users can effortlessly locate charging stations, assess quality through reviews and ratings, and plan charging sessions based on real-time availability. The battery voltage of 45.2 V is a critical parameter for monitoring the health and performance of the battery, influencing the accuracy of state of charge (SoC) estimations and potentially impacting the efficiency of the electric vehicle. The 47.7 km driven is a key factor in assessing energy consumption and vehicle efficiency, which can affect the remaining state of charge in the battery. The battery's state of charge (SOC) is at 85%, indicating a relatively high charge level. Knowing that the charging station is available is crucial for planning charging activities, allowing users to proceed without concerns about station availability. The booking time at 10:00 AM is essential for efficiently managing charging infrastructure, especially in scenarios with high demand for charging services. These data points collectively contribute to optimizing the charging experience and ensuring the effective utilization of electric vehicle resources. Conclusion:: The proposed EVs Charging Finder App offers a practical and efficient solution to address the surging demand for charging stations. By providing comprehensive information and real-time alerts, this system aims to make EV charging more accessible, user-friendly, and environmentally sustainable.

EVaaS: Electric vehicle-as-a-service for energy trading in SDN-enabled smart transportation system

The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC) stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station’s density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive adoption of electric vehicles.

Thailand has targeted having 1.2 million electric vehicles (EVs) on the road by 2036. In accordance with this government policy, the Department of Land Transport has partnered with the business sector to provide 100% EV taxis to encourage greater use of EVs. Ensuring there are sufficient charging stations is one of the critical factors for the adoption of EVs. Utilizing qualitative research, this paper studies the EV situation in Thailand, the expansion of EV taxis in Thailand, and the optimal site selection criteria for the installation of EV taxi charging stations. Regarding the EV taxi situation in Thailand, it was found that the expansion of EV taxis in Thailand can be divided into 2 cases: by the private sector without government promotion or support and by the private sector with government promotions and support. EV Society is the first and only company to run an EV taxi service with 100 units, mainly operating out of Suvarnabhumi Airport. According to interviews with key stakeholders, the optimal locations for the installation of charging stations are airports, main roads, tourist routes, normal taxi routes, taxi garages or nearby areas, and LPG/NGV/gas stations. These locations require enough parking space, easy access, sufficient power supply, and appropriate facilities, while they should also be scattered throughout the city at strategic locations within optimal distances of each other. Moreover, this study found that the key success factor for optimal site selection involves location analysis of the busiest taxi commuter routes to identify the routes that are in highest demand. Previous installation programs have involved large-scale investment but the stations were inefficient and had little usage in return. The investment efficiency can be improved by conducting applicable site selection analysis before installing charging stations.