Optimal Planning for Public Charging Infrastructure by Behaviour‐Based Electric Vehicle Charging Demand Simulations and Geographic Information System

The planning of public charging stations is crucial for the growth of electric vehicles (EVs). To improve the accuracy of predicting EV charging demand in urban areas, we propose a charging decision-making model based on fuzzy logic. Meanwhile, the influence of private charging piles is considered to further enhance the accuracy of predicting public charging demand. To address the issue of optimization algorithms easily getting stuck in local optima due to the vast quantity of variables involved in the location and capacity planning process, this paper introduces a two-layer planning method. In specific, the upper-level location model optimizes the locations of charging stations, while the lower-layer capacity model determines the number of charging piles within each station, leading to a reduction in the number of variables for each layer. Moreover, through iterative exchange results between the upper-layer location model and lower-layer capacity model, the optimal solution can be attained. The simulation results demonstrate that the proposed method can simultaneously consider the perspectives of both EV drivers and charging station investors, while also enhancing the utilization rates of public charging piles.

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.

In recent years, environmental issues have gradually become one of the most concerning issues in the world. New energy vehicles have attracted wide attention by their good environmental and social benefits such as zero exhaust emissions and low noise pollution. This paper proposes a regional charging demand forecasting method for electric vehicles (EVs) based on hierarchical charging decision model to solve the problem of charging pile capacity planning, which affects the charging decision behavior of EVs users. The Monte Carlo sampling model is adopted to obtain the time distribution of electric vehicle arriving at each functional area, the distribution of battery state of charge when arriving, and the distribution of average daily mileage. The negative exponential distribution model is used to obtain the average mileage distribution. The fuzzy system theory is proposed to calculate the charging probability, and the fuzzy rule base is adjusted according to the actual data to ensure that the fuzzy rule base is real and effective. Finally, the charging demand distribution curve of electric vehicles in each functional area is obtained by sampling simulation. Through experimental analysis, optimizing the capacity configuration of charging facilities in the station can well describe how electric vehicle users make charging decisions in the actual situation. The research on electric vehicle charging stations can slow down the waiting time of electric vehicle charging, and then affect the process of replacing fuel vehicles with electric vehicles to achieve the development goal of popularizing electric vehicles. It is of great significance for the promotion of new energy vehicles to rationally plan the location and capacity of electric vehicle charging stations.

Electric vehicle (EV) smart charging, which regulates the charging rates of EVs in response to the availability of surplus solar photovoltaics (PV) power or the electricity prices, can effectively enhance the local power demand–supply balance and help improve the PV power local utilization. In this regard, researchers have developed two categories of EV charging controls: (i) B2V or G2V ((i.e., building-to-vehicle or grid-to-vehicle) power flow in which the EV can only be charged; and (ii) B2V2B or G2V2G (i.e., building to vehicle to building or grid-vehicle-to-grid) in which the vehicles can be both charged and discharged. The frequent charging/discharging could potentially accelerate the EV battery degradation, which might make such applications not economical. However, systematic investigation has rarely been conducted for the impact of various EV usage and charging factors (including the EV charging strategy, different EV charging forms, EV charging limits, and commuting distance) on the power balancing performances and EV battery cycling degradation. As a result, the EV owners may not be willing to join the smart charging demand response due to the concerns of accelerated battery degradation, and this hinders the applications of EVs in the power regulation in the future energy system. This chapter aims to investigate the effect of different ways of using EVs on the demand response performances and the EV battery degradation. A parametric study considering a set of different scenarios combining various EV charging forms, EV charging limits and commuting distances will be conducted in Sweden. A smart charging control method of the EV will be developed, which can optimize the EV charging and discharging rates to minimize the grid interactions. A degradation model, which can evaluate the EV battery degradation due to charging/discharging cycling, will be constructed to investigate the EV battery degradation under typical scenarios. The performances of each scenario will be analyzed and compared to draw conclusions. The study results can help improve researchers’ understanding of the impacts of smart EV charging in the building community performances. The obtained impacts on the battery degradation can also support decision makers in selecting suitable EV charging and usage strategies.

A Blockchain Based Electric Vehicle Smart Charging System with Flexibility

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.

The transition to sustainable transportation has fueled the need for innovative electric vehicle (EV) charging solutions. Building Integrated Photovoltaics (BIPV) systems have emerged as a promising technology that combines renewable energy generation with the infra-structure of buildings. This paper comprehensively reviews the BIPV system for EV charging, focusing on its technology, application, and performance. The review identifies the gaps in the existing literature, emphasizing the need for a thorough examination of BIPV systems in the context of EV charging. A detailed review of BIPV technology and its application in EV charging is presented, covering aspects such as the generation of solar cell technology, BIPV system installation, design options and influencing factors. Furthermore, the review examines the performance of BIPV systems for EV charging, focusing on energy, economic, and environmental parameters and their comparison with previous studies. Additionally, the paper explores current trends in energy management for BIPV and EV charging, highlighting the need for effective integration and recommending strategies to optimize energy utilization. Combining BIPV with EV charging provides a promising approach to power EV chargers, enhances building energy efficiency, optimizes the building space, reduces energy losses, and decreases grid dependence. Utilizing BIPV-generated electricity for EV charging provides electricity and fuel savings, offers financial incentives, and increases the market value of the building infrastructure. It significantly lowers greenhouse gas emissions associated with grid and vehicle emissions. It creates a closed-loop circular economic system where energy is produced, consumed, and stored within the building. The paper underscores the importance of effective integration between Building Integrated Photovoltaics (BIPV) and Electric Vehicle (EV) charging, emphasizing the necessity of innovative grid technologies, energy storage solutions, and demand-response energy management strategies to overcome diverse challenges. Overall, the study contributes to the knowledge of BIPV systems for EV charging by presenting practical energy management, effectiveness and sustainability implications. It serves as a valuable resource for researchers, practitioners, and policymakers working towards sustainable transportation and energy systems.

Deploying an adequate electric vehicle (EV) charging infrastructure to support the increasing EV market is one of the major strategic goals of the U.S. government. This requires a well-designed EV charging network. The distribution and capability of the existing charging networks in terms of EV population, location, charging rate, and time of charging in San Diego is examined. A mathematical model to calculate the demand number of public Level 2 chargers universally applicable is developed. The study showed that although San Diego has sufficient chargers to accommodate the existing EV’s charging demand, the current public charging distribution network is neither well designed nor effectively used. To eliminate the waste resulting from the inefficiently designed charging infrastructure and maximize the usage rate of each charger, it is recommended that the designed optimal model to be utilized and the charging location priority be implemented to improve the availability and accessibility of charging network in the City of San Diego.ᅟIntroduction:The purpose of this study is to identify current problems with the existing electric vehicle public charging stations and come up with solutions to improve the availability and accessibility of public charging stations in the City of San Diego. The objective of this research project is also to develop a mathematical model to predict the demand of EV chargers in any city including in the City of San Diego.Methods:A mix of quantitative and qualitative research methods are used to analyze the problem. The first phase of this project is to determine the study area by identifying the existing problems and issues from existing sources, and formulating hypothesis.Results:The distribution and capability of the existing charging networks in terms of EV population, location, charging rate, and time of charging in San Diego was examined. A mathematical model to calculate the demand number of public Level 2 chargers for the City of San Diego and for each zip code was developed. Among 361 tested public Level 2 chargers distributed in 34 communities, 66 chargers located at 37 charging stations distributed in 22 communities were found to be nonoperational or damaged but still operational. They accounted for 18% of the total number of tested EV charging stations and 12.7% of the total public Level 2 in San Diego. The model tested using data from San Francisco Bay Area, and Los Angeles County matched well to the predictions.Conclusions:The conclusion is that although San Diego has sufficient chargers to accommodate the existing EV’s charging demand, the current public charging distribution network is neither well designed nor effectively used. To eliminate the waste resulting from the inefficiently designed charging infrastructure and maximize the usage rate of each charger, it is recommended that the designed optimal model to be utilized and the charging location priority be implemented to improve the availability and accessibility of charging network in the City of San Diego. This model is easily applicable in the European environment since all the five significant independent variables (B/E - Battery capacity to EV Range Ratio, D-Driver Traveling Distance, β - Ratio of EV driver charges away from home, PrefL2- percentage that EV driver prefers to charge on Level 2 stations, and TL2- duration of public Level 2 chargers’ work per day) are easy to obtain. Hence this proposed model has universal applicability.

Electric vehicle park-charge-ride programs: A planning framework and case study in Chicago

In many metropolitan cities, multi-unit residential buildings (MURB) are becoming more common than single-family independent homes due to lack of urban space. MURB residents (around 42% in Europe) are potential adopters of electric vehicles (EV), but lack a private garage for EV charging. They need to exclusively rely on public charging, which currently serves only 5% of EVs. As EVs become more prevalent, the lack of extensive public charging can create a short-term demand-supply mismatch in specific city neighbourhoods, as well as preclude long-term growth in EV adoption. We believe that uberization of private garage chargers that are typically under-utilized during day-time can alleviate this problem. In this work, we examine how a charging service provider can match public charging demand with private suppliers while using a demand-response based pricing model. We base our study on real-world traffic patterns for the city of Luxembourg by augmenting the Luxembourg SUMO traffic scenario (LuST) simulator. Specifically, an EV's charging demand is modeled by a state machine with charge/discharge dynamics based on Tesla Model-S. Our preliminary results suggest that the proposed uberization strategy has the potential to gracefully handle demand spikes with higher revenue yield for a charging service provider, even while handling different categories of service users.

Global climate change is affecting human life more seriously than ever before. Countries around the world have identified cars as a significant source of pollution, leading to an increased interest in eco-friendly automobiles. Electric vehicles (EVs), which are characteristically eco-friendly, have become a choice for future transportation system. In this paper, we proposed a strategy for determining the appropriate placement of large-scale charging stations using K-mean algorithm. Also, the initial results were validated utilizing actual electricity usage data from the existing chargers. Currently in Korea, a typical public charging station has two to three chargers to support EV users. As EVs become more popular, new problems arise, such as charger hopping and/or long waiting lines. In order to address these new issues, a large-scale charging station concept which houses more than ten chargers, is suggested. In doing so, a strategic approach for selecting close-to-ideal locations for the charging stations is introduced to maximize the charging station’s effectiveness. In this study, Jeju Island, Korea, which has many EVs, was used as a testbed. With the wealth of EV chargers and their usage data, initial validation of the proposed methodology was made possible. During the evaluation of the best possible locations for the largescale charging stations, we considered the locations of tourist attractions and convenient support facilities, as well as the population. After the evaluations, the proposed locations were validated using actual long-term charger usage data on Jeju Island. The demonstrated strategy for identifying appropriate locations for large-scale charging stations can be used by other tourist heavy islands or even small countries.

The need for deploying fast-charging stations for electric vehicles (EVs) is becoming essential in recent years. This need is justified by the increasing charging demand and supported by new charging technologies making EV chargers more efficient. In this paper, we provide a survey on EV fast-charging models and introduce a data-driven approach with an optimization model for deploying EV fast-chargers for both electric vehicles and heavy trucks traveling through a network of suburban highways. This deployment aims at satisfying EV charging demands while respecting the limits imposed by the electric grid. We also consider the availability of local photovoltaic (PV) farm and integrate its produced power to the proposed charging network. Finally, through a case study on Paris-Saclay area, we provide locations for EV charging stations and analyze the benefits of integrating PV power at different prices, production costs and charging capacities. The obtained results also suggest potential enhancements to the charging network in order to accommodate the increasing charging demand for EVs in the future.

Electric vehicle demand estimation and charging station allocation using urban informatics

Siting public electric vehicle charging stations in Beijing using big-data informed travel patterns of the taxi fleet

Electric vehicles are becoming more popular because they are not only helping countries’ economies by lessening dependency on oil but also helping in creating clean environments that are more habitable and sustainable. One of the most crucial issues in promoting the usage of electric vehicles is the availability of charging stations (CS). This study proposed to use GIS-MCDM methods to produce a model that could be used for selecting appropriate locations for EVCS. MCDM AHP, and FAHP techniques will be used to weigh the criteria regarding accessibility and influence on environment. Four main criteria are selected for this work these are environmental, geographical, urbanity, and transportation, the weight of different criterion will be determined. The results will be integrated into GIS for the selection of various suitable locations for EV charging stations. TOPSIS will be used to rank the sites and choose the best locations for charging stations. At the end of the study, it is expected to have a reliable model for the selection of suitable locations for EVCS, which could be used for the selection of proper location for EVCS, for efficiency and effectiveness of electric vehicles charging within cities and along highways, this will improve the adoption and acceptability of EV across the world.