Determinant Factors in Estimating Charging Stations for Electric Vehicles in Bogotá Using Voronoi Regions

To reduce the negative effects caused by electric vehicles (EVs), this paper proposes a coordinated charging strategy for batteries in EV charging station. According to the charging characteristics of EV batteries, the strategy is established to minimize the sum of squares of the substation, which could consider the number of battery chargers, EVs' needs for batteries, power limit of EV charging and switching station and substation, node voltage and so on. By combining the method of power flow linearization and genetic algorithm, the optimal charging starting time of each battery can be obtained quickly, which could reduce the sum of squares and P-V difference of power grid. The correctness and efficiency of the proposed strategy are validated by a EV charging and switching station as a case.

Electric vehicle attributed future air pollution alleviation: A case study in Guangdong province, China.

Abstract: Increasing numbers of individuals are adopting electric vehicles (EVs) due to climate change and environmental concerns. This has generated a high demand for efficient, easy-to-use, and smart charging stations. This project, Electric Vehicle Charging Station Point, provides a web application that helps users in finding nearby EV charging stations and repair stations quickly and accurately. Having electric car charging station infrastructure, or electric vehicle points (EVPs), is necessary for more individuals to adopt electric vehicles. Designing and maintaining electric vehicle points involve numerous challenges, including power quality, security, stability, communication, and interfacing with other systems. To overcome these challenges and gain the maximum benefits from electric vehicle points, various technologies and protocols can be utilized. This project helps in making smart and sustainable transport by providing easy access to EV charging stations and promoting the use of clean transport modes. Key Words: Electric Vehicle(EVs), Charging Station Locator, Sustainable Transportation, Leaflet.js Map Integration, Admin Panel, Chatbot assistance.

Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach

Widespread adoption of electric vehicles (EVs) would significantly increase the overall electrical load demand in power distribution networks. Hence, there is a need for comprehensive planning of charging infrastructure in order to prevent power failures or scenarios where there is a considerable demand-supply mismatch. Accurately predicting the realistic charging demand of EVs is an essential part of the infrastructure planning. Charging demand of EVs is influenced by several factors such as driver behavior, location of charging stations, electricity pricing etc. In order to implement an optimal charging infrastructure, it is important to consider all the relevant factors which influence the charging demand of EVs. Several studies have modelled and simulated the charging demands of individual and groups of EVs. However, in many cases, the models do not consider factors related to the social characteristics of EV drivers. Other studies do not emphasize on economic elements. This thesis aims at evaluating the effects of the above factors on EV charging demand using a simulation model. An agent-based approach using the NetLogo software tool is employed in this thesis to closely mimic the human aggregate behaviour and its influence on the load demand due to charging of EVs. EV charging stations where the EV charging takes place will play an important role in the energy management of smart cities. Private and commercial EV charging loads would further stress the distribution system. Photovoltaic (PV) systems, which can reduce this stress, also show variation due to weather conditions. Hence, after the successful modelling of EV charging behavior using agent based approaches, a hybrid optimization algorithm for energy storage management is proposed as an application. This algorithm shifts its mode of operation between the deterministic and rule-based approaches depending on the electricity price band allocation. The cost degradation model of the energy storage system (ESS) along with the levelized cost of PV power is used in the case of PV integrated charging stations with on-site ESS. The algorithm comprises three parts: categorization of real-time electricity price in different price bands, real-time calculation of PV power from solar irradiation data and optimization for minimizing the operating cost of an EV charging station integrated with PV and ESS. An extensive simulation study is carried out with private and commercial EV charging load model obtained from the agent based modeling approach, in the context of Singapore, to check the effectiveness of this algorithm. Furthermore, a detailed analysis of the subsidy and incentive to be given by the government agencies for a higher penetration of PV systems is also presented. This work would aid in planning of adoption of PV integrated EV charging stations with on-site ESS which would be expected to take place of traditional gas stations in future.

The charging facilities should be available at appropriate locations to provide better services to the electric vehicle (EV) users. Also, optimally allocated charging stations are necessary in distribution network to maintain the power system healthy. In this work, a distribution network is taken to place charging station at optimal nodes and a road network is considered to simulate traffic flows. The distribution network is overlapped with the road network. Moreover, renewable based distributed generators (DGs) are allocated simultaneously. The main objective of this work is to reduce the energy loss of the distribution system. Two-stage model is used, where stage-1 is to allocate EV charging stations and DGs, and stage-2 is to allot EVs at appropriate charging station, based on the travel distances considering traffic flow. Arithmetic optimization algorithm for stage-1 and integer linear programming for stage-2 are used to solve the problems. The uncertainties are taken care by 2m point estimation method. Results show the energy loss reduction and voltage profile improvement due to optimal allocation of charging station in presence of DGs.

All people around the world requires to move from one place to another for their daily needs. To overcome this requirement conventional vehicles were developed. These vehicles run only by the means of fossil fuels such as petrol, diesel, gas etc. By consuming these fuels, they will exhaust CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> as their biproduct which causes global warming, release of greenhouse gas and other harmful effects. This leads to the invention of the Electric Vehicles (EV). This E-vehicles consumes only the electricity to run, and it does not require any lubricants and fuels. The main drawback of the Electric vehicles is the running time is limited due to the less amount of range in a charge. Electric vehicle charging stations are fewer compared to the petrol/diesel bunk at present. One of the main challenges for the electric vehicle is charging their vehicles in the charging stations and it involves added construction cost and requires lots of space while constructing a charging station for an individual battery voltage type. In a rare case if an Electric vehicle charging station is spotted, it may not be available for the all the type of electric vehicle models, it might vary according to the company models availability. This leads the driver to look for other charging station or upcoming charging station and there is huge demand on the particular or selective charging station. To overcome this situation, this study has built a prototype to enhance all the electric vehicle two-wheelers to charge their vehicles in a single charging station. The proposed model can charge the two-wheelers, which has their input voltages as 52V, 60V, 72V volts. These three voltages are selected based on the trending Electric Vehicles (EVs). 52V represent the Ather 450x model, 60V represent the TVS Iqube model, and 72V represent the Hero Photon model. To develop a feasible solution, this study has used the multi tap changing transformer to control these three-output voltage to the vehicle, which can be easily charged. Further, this study has created a mobile application to check the battery status and display the battery percentage while charging and time left for charging. This application can change the required input voltage at the time of charging. In addition to, this research work has implemented a special feature, which is auto cut off, which shutdowns the supply when the vehicles battery is fully charged. This avoids the critical situation of bursting of batteries when they are overloaded. This special feature is a feather in hat in our prototype. There are many advantages in using this type of charging station like charging the electric vehicle batteries, which contain different voltages such as 52, 60, 72 Volts, auto cut-off process is implemented and mobile application is used to switch the required voltages. The only demerit while using this system is the proposed model cannot be able to charge more than one vehicle at a time.

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.

With the upcoming implementation of the amendment to Regulation (EU) 2019/631 of the European Parliament and of the Council, from 2035 there will be a ban on the registration of new vehicles with internal combustion engines (ICE) in the Member States of the European Union (EU). Consequently, changes in the transportation sector, resulting from the increasing use of electric vehicles, appear to be inevitable. According to the adopted legal acts, the European Union Member States will be obliged to develop, among others, a charging infrastructure and access to public charging stations for electric vehicles. As a result, there will be a need to ensure a significant increase in the power and the number of charging stations and to determine their appropriate location. The article presents the challenges faced by charging station operators and difficulties related to the further development of electric vehicle charging infrastructure in Poland. The still poorly developed public charging infrastructure for electric vehicles, especially in service areas located along the main communication routes, remains the main obstacle to the development of electromobility. In the context of legal, financial, technological, and organizational challenges, the problem of the proper distribution of electric vehicle charging stations along the main communication routes is therefore of particular importance. The aim of the article is to present a new, proprietary method for determining the location of electric vehicle charging stations in Poland within the Trans-European Transport Network (TEN-T), which considers objective location factors: adherence to AFIR requirements, the specificity of the Polish power system and existing parking infrastructure. As a result of using the developed method, a list of 188 recommended locations for the construction of electric vehicle charging stations in Poland along the Trans-European Transport Network (TEN-T) was created. It has been shown in this way that the use of the presented method enables the suitable determination of the location of electric vehicle charging stations along transport routes, considering legal, financial, and technological requirements, which will significantly facilitate the operation of zero-emission transport.

Electric vehicle (EV) customers are expected to charge EV batteries at a rapid EV charging station or via on-road wireless EV charging systems when possible, as per their charging needs to successfully complete any remaining trips and reach their destination. When on-road wireless EV charging systems are considered as an alternative charging method for EVs, this can affect the load of a rapid EV charging station in terms of time and magnitude. Hence, this paper presents a probabilistic framework for estimating the arrival rate of EVs at an EV rapid charging station, considering the availability of on-road wireless charging systems as an alternative charging method. The proposed model incorporates an Electric Vehicle Decision Tree that predicts the times when EVs require rapid charging based on realistic transportation data. A Monte Carlo simulation approach is used to capture uncertainties in EV user decisions regarding charging types. A queuing model is then developed to estimate the charging load for multiple EVs at the charging station, with and without the consideration of on-road EV wireless charging systems. A case study and simulation results considering a 32-bus distribution system and the US National Household Travel Survey (NHTS) data are presented and discussed to demonstrate the impact of on-road wireless EV charging on the loads of an rapid EV charging station. It is observed that having on-road wireless EV charging as complementary charging to EV charging stations helps to significantly reduce the peak load of the charging station, which improves the power system capacity and defers the need for system upgrades.

Charging stations deployment is an important problem in Electric Vehicle (EV) networks. The distribution of EV is complicated in urban environments. Therefore, reasonable location deployment will avail to reduce construction costs and improve user experience. Aim to this, this paper comprehensively considers the cost of charging stations and the charging costs of EVs. Studied the charging station location, charging station capacity and the optimization algorithms for charging station location, and proposed a method for estimating the optimal location and optimal capacity allocation of EV charging stations. Firstly, this paper uses the Voronoi diagram to divide the service range of the charging stations, then uses the differential evolution algorithm combined with the particle swarm optimization algorithm (DEIPSO) to solve the charging station location model, and finally consider the residence time of EV in the charging station, use queuing theory to solve the charging station capacity allocation model. The experimental results shows that DEIPSO can better jump out of the local optimum and achieve the global optimum; the proposed model can plan the charging station on the basis of fully considering the total charging costs of charging stations and EVs.

Electric vehicles are widely regarded as pivotal in driving the sustainability of transportation networks forward, thanks to their capacity to diminish carbon emissions, enhance air quality, and bolster the robustness of electricity grids. The accessibility of charging infrastructure and the subjective norms that endorse electric mobility actively shape the electric vehicles acceptance. In this study, Our main goal is to provide off-grid electric vehicle charging infrastructures and the data communication protocols that connect to servers. We analyze the specifications of the OCPP (Open Charge Point Protocol) with an emphasis on its applicabillity for electric charging stations for vehicles. Our research concludes that off-grid electric vehicle charging systems can be effectively applied to small electric vehicles such as electric motorcycles, scooters, and bicycles. The OCPP data communication protocol can also support interactions between small electric vehicle charging stations and central server management systems (CSMS). Furthermore, we tested the electric vehicle charging process for a duration of two hours, and the charging station consistently produced stable voltage, current, and power output, matching the inverter outputs and fulfilling the specifications required by electric vehicle charging adapters. Analysis of throughput data indicates a positive correlation between the number of operational ports at a charging station and the volume of data processed by the server. However, beyond a certain threshold a decline in data transactions was observed, attributable to data loss.

Problem. The article proposes a single-link structure for an electric vehicle charging station utilizing an active four-square rectifier with power factor correction. A Matlab model of the proposed charging station is developed, taking into account parameters such as the power network, the switches of the active rectifier, its automatic control system, and an equivalent model of the battery compartment. Additionally, a mathematical model for calculating static and dynamic losses is created based on polynomial approximation of the energy dependencies of IGBT modules. The analysis investigates power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Goal. The aim of this study is to propose a single-link structure for an electric vehicle charging station using an active four-square rectifier with power factor correction. It includes an analysis of power quality parameters, components of energy losses, and efficiency of the charging station at different charge currents and PWM frequencies during a full battery charge interval. Methodology. To achieve the goal, several key steps are considered. These include theoretical substantiation of the scheme of the electric microgrid charging station for electric vehicles with one-stage energy conversion, analysis of the battery connection scheme in the Tesla Model S electric car, research and calculation of efficiency, modeling of the charging station, development of a Matlab model of a microgrid system for the charging station, SAC analysis of battery charge voltage and current of a three-phase AV with PWM, modeling of losses in IGBT modules by polynomial approximation of dependencies, distribution of losses in the charging station system, and analysis of energy efficiency parameters. Results. The study presents the energy efficiency parameters of an external DC EV charging station using an active rectifier. It reveals that maximum efficiency of the system is achieved at minimum charge current. However, decreasing the charge current prolongs the charge process and slightly affects power quality parameters. Originality. A mathematical model for calculating static and dynamic losses was developed based on polynomial approximation of the energy dependencies of IGBT modules. The analysis encompasses power quality parameters, components of energy losses, and efficiency of the charging station across various charge currents and PWM frequencies during a full battery charge interval. Practical value. This study contributes to the further development of electric vehicles by improving the energy indicators of electric vehicle batteries and converters of electric vehicle charging stations, enabling fast charging modes. Active development is observed in each of these directions.

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.

Research on location planning of urban charging stations and battery-swapping stations for electric vehicles