Electric Vehicle Supply Equipment Research Articles

Electric vehicle supply equipment location and capacity allocation for fixed-route networks

Electric vehicle (EV) supply equipment location and allocation (EVSELCA) problems for freight vehicles are becoming more important because of the trending electrification shift. Some previous works address EV charger location and vehicle routing problems simultaneously by generating vehicle routes from scratch. Although such routes can be efficient, introducing new routes may violate practical constraints, such as drive schedules, and satisfying electrification requirements can require dramatically altering existing routes. To address the challenges in the prevailing adoption scheme, we approach the problem from a fixed-route perspective. We develop a mixed-integer linear program, a clustering approach, and a metaheuristic solution method using a genetic algorithm (GA) to solve the EVSELCA problem. The clustering approach simplifies the problem by grouping customers into clusters, while the GA generates solutions that are shown to be nearly optimal for small problem cases. A case study examines how charger costs, energy costs, the value of time (VOT), and battery capacity impact the cost of the EVSELCA. Charger equipment costs were found to be the most significant component in the objective function, leading to a substantial reduction in cost when decreased. VOT costs exhibited a significant decrease with rising energy costs. An increase in VOT resulted in a notable rise in the number of fast chargers. Longer EV ranges decrease total costs up to a certain point, beyond which the decrease in total costs is negligible.

Analyzing the urban-rural divide: Understanding geographic variations in charging behavior for a user-centered EVSE infrastructure

Understanding charging behavior in relation to its geographic locale is crucial for fostering a user-centered and equitable electric vehicle supply equipment (EVSE) infrastructure. This study employs a comprehensive and multivariate approach to investigate the utilization of public Level 2 electric vehicle chargers, revealing notable differences in charging behavior across provinces and varying urbanity levels in Canada. The findings showcase a clear urban-rural divide in charging behavior, characterized by distinct patterns across different provinces. While a weekly repetitive charging demand pattern is evident, high-demand periods diverge among charging networks. Furthermore, the study identifies charging demand trends within these networks, which display variations in direction and shape. The results of this study indicate that charging behavior is inconsistent between different charging networks, indicating that stakeholders should recognize the necessity of developing individualized and area-specific charging networks to bolster utilization and enhance usability. As the first study, to the best of the authors' knowledge, to explore geographic variations in charging behavioral patterns, this study serves as a crucial stepping stone towards establishing an inclusive, user-centric, and accessible EVSE infrastructure for all.

EVSE Effectiveness in Multi-Unit Residential Buildings Using Composite Optimization and Heuristic Search

The effectiveness of electric vehicle supply equipment (EVSE) is a very important factor in multi-unit residential buildings (MRBs) when planning to invest in an electric vehicle (EV). However, the expected benefits depend on not only technology but also need many non-functional requirements such as building facilities, electrical infrastructure, operating costs, and energy demand management of EVSE. We discuss the functional and non-functional constraints in utilizing EVSEs in MRBs and addresses them by constructing a composite optimization problem with a heuristic boundary selection. The proposed model urges the use of electrical safety codes –to mitigate the hazard risk– and promotes deploying a flexible and scalable energy management system (EMS) to schedule, reserve, and tune the charging sessions. This article provides an effective EMS for mitigating the energy demand growth caused by EV charging on MRBs, by finding the equilibrium number of EVSEs and their energy usage through a heuristic search. The proposed EMS is reinforced by embedding machine learning tools such as k-means, silhouette scoring, heuristic search, and autoregressive model to adjust the optimal operational state of EVSEs. We delineated the proposed framework and the outcomes using two case studies with real data. The results showed that more EVSEs can be utilized in MRBs and 100% EVs can be fully charged with a lower capital cost and no additional energy demand cost.

TLS-handshake for Plug and Charge in vehicular communications

Electric vehicle technology has brought some important issues to order. Securing the communication between electric vehicle and charging station is an important one. The International Organization for Standardization released ISO 15118 standard to specify the communication between electric vehicles and electric vehicle supply equipment. The ISO 15118 standard requires Transport Layer Security function for secure charging communication between electric vehicle and charging station as mandatory. The Transport Layer Security requirements have been completely changed with the latest version of the standard (ISO 15118-20) and have not been clearly described and implemented yet. The implementation of the function causes an inevitable vulnerability due to non-functioning authentication resulting with the entire vehicle function and security system can suddenly stop working. For this reason, new Transport Layer Security protocol must urgently be analyzed, developed, integrated, and implemented.This study proposes solutions for the development, implementation, realization of Transport Layer Security handshake requirements and functions between electric vehicle and charging station with ISO 15118 protocol. The new and existing Transport Layer Security requirements, analyzed, evaluated and the new Transport Layer Security functions derived and fully described. Based on these requirements and functions, a Transport Layer Security communication sequence and processes are developed and implemented and validated on the test bench. The validation proved sufficient implementation of the Transport Layer Security functions (successful authentication, exchange of cipher suite parameters and generation of the TLS master key). The messages were prosperous encrypted and decrypted with the generated master key. Thus, Transport Layer Security handshake is developed, implemented, and validated with this study.

A Federated Byzantine Agreement Model to Operate Offline Electric Vehicle Supply Equipment

A Federated Byzantine Agreement Model to Operate Offline Electric Vehicle Supply Equipment

Revolutionizing mobility: a comprehensive review of electric vehicles charging stations in India

An Electric Vehicle (EV) charger or Electric Vehicle Supply Equipment (EVSE) is a piece of equipment that supplies electrical power for charging plug–in electric vehicles. Although batteries can only be charged with Direct Current (DC) power, most electric vehicles have an onboard Alternative Current AC—to—DC converter and most fully electric cars can accept both AC and DC power. The adoption of EVs can bring about significant relief in noise pollution and also environmental pollution if the required electricity is generated using renewable sources. DC charging stations of various levels are commonly equipped with multiple ports of various levels to be able to charge a wide variety of EVs. EVSEs are found at various facilities such as street–side or retail shopping centers, government facilities, and other parking areas. To ensure a sustainable environment by reducing the carbon emissions from vehicles, the use of EVs needs to be promoted. The need for having Electric Vehicle Charging Stations (EVCS) in any region depends upon the demand and cluster density of EVs in that region and is a major factor in the process of promoting the use of EVs and facilitating sustainable tourism using cleaner fuels. The authors of this study have located the various types and numbers of EVSEs throughout all the states and union territories of India, showing the emerging use of EVs so that EV users can conveniently locate charging stations and plan their routes accordingly. Furthermore, other citizens may be encouraged to own and use EVs for better environmental sustainability.

A Geo‐Spatial Method for Calculating BEV Charging Inconvenience using Publicly Available Data

ABSTRACTAs governments and the automotive industry push towards electrification, it becomes increasingly critical to address the factors which influence individual car buying decisions. Evidence suggests that operational inconvenience or the perception thereof plays a large role in consumer decisions concerning battery electric vehicles (BEVs). BEV ownership inconvenience and its causal factors have been relatively understudied, rendering efforts to mitigate the issues insufficiently informed. This paper presents a method of producing an empirical equation which relates operational inconvenience to a small number of housing and local electric vehicle supply equipment (EVSE) infrastructure factors. The paper then further provides a method of applying the equation in a geo‐spatial context allowing for the evaluation of the effects of policies in a geographical manner. this method enables future quantitative analyses concerning investment in EVSE infrastructure to be directly sensitive to BEV operational inconvenience due to charging.

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Electric Vehicle Supply Equipment Day-Ahead Power Forecast Based on Deep Learning and the Attention Mechanism

Energy Management Strategies of Solar Rooftop Campus with Battery Energy Storage System (BESS) And EV Supply Equipment (EVSE) using HOMER

Commercial sector has been among the largest source of electrical load demand in Malaysia including educational buildings. Nowadays, distributed energy resources, such as solar photovoltaics (PV) and battery energy storage systems (BESS), are being implemented in educational campus to save and manage its energy consumption. Additionally, electric vehicles (EV) are becoming more popular as they are more environmentally friendly than the traditional gasoline-powered cars. This research aims to examine the cost-effectiveness, environmental and technical aspects of a microgrid system that includes grid-connected solar panels, BESS and EV supply equipment (EVSE). The study was conducted for Universiti Teknologi MARA campus Pulau Pinang. The campus microgrid models are simulated using HOMER software to explore four different of microgrid configurations. The load profiles of the buildings in the campus were gathered and used in the model to identify the optimum microgrid model. The findings show Grid-PV-BESS has the best hybrid energy system based on the weightage sum decision making approach considering multiple criteria assessments. Through this study, a sustainable hybrid energy system in the educational campus can be proposed integrating solar PV, BESS and EVs.

The Current State of Light-Duty Electric Vehicle Supply Equipment Costs: An Assessment of Contemporary Understanding

This study uses a hybrid meta-analysis and literature-review approach to understand the current state of knowledge regarding the costs of electric vehicle supply equipment (EVSE). We present a novel way to consider, categorize, and label measures of cost and show cost measure estimates from a sample of 13 recent studies. We find that in general, there is too much variation and too few commonly represented EVSE cost measures to reasonably provide aggregate figures for these measures. We propose a convention for presenting EVSE cost measures that includes the application (commercial or residential), the power level (Level 1, Level 2, DCFC [with further distinction based on rated power capacity]), and the type of cost measure (hardware, installation, operation, and total cost). We contend that providing researchers with standard cost measures will help to advance our knowledge of EVSE costs by ensuring that future work will use common metrics. Establishing common metrics will enable conventional meta-analyses that will make assessments of EVSE costs even more accessible. Additionally, common metrics will make tracking costs more reliable as the technology continues to evolve and become more ubiquitous.

Optimal Charging Station Placement and Scheduling for Electric Vehicles in Smart Cities

Electric vehicles (EVs) have emerged as a transformative solution for reducing carbon emissions and promoting environmental sustainability in the automotive industry. However, the widespread adoption of EVs in the United States faces challenges, including high costs and unequal access to charging infrastructure. To overcome these barriers and ensure equitable EV usage, a comprehensive understanding of the intricate interplay among social, economic, and environmental factors influencing the placement of charging stations is crucial. This study investigates the key variables that contribute to demographic disparities in the accessibility of EV charging stations (EVCSs). We analyze the impact of various factors, including EV percentage, geographic area, population density, available electric vehicle supply equipment (EVSE) ports, electricity sources, energy costs, per capita and average family income, traffic patterns, and climate, on the placement of EVCSs in nine selected US states. Furthermore, we employ predictive modeling techniques, such as linear regression and support vector machine, to explore unique nuances in EVCS installation. By leveraging real-world data from these states and the identified variables, we forecast the future distribution of EVCSs using machine learning. The linear regression model demonstrates exceptional effectiveness, achieving 90% accuracy, 94% precision, 89% recall, and a 91% F1 score. Both graphical analysis and machine learning converge on a significant finding: Texas emerges as the most favorable state for optimal EVCS placement among the studied areas. This research enhances our understanding of the multifaceted dynamics that govern the accessibility of EVCSs, thereby informing the development of policies and strategies to accelerate EV adoption, reduce emissions, and promote social inclusivity.

Artificial deep neural network enables one-size-fits-all electric vehicle user behavior prediction framework

As greener mobility becomes the norm with the advent of electric vehicles (EVs), a natural question arises: how big of a change are we seeing in terms of the stochastic energy demands imposed by EVs? There have been considerable difficulties in analyzing the adoption of all types of EV infrastructure due to the lack of publicly available individual EV user data across various service providers, such as distribution network operators, EV aggregators, EV users, utilities, regulators, and academics. In this study, we introduce the JETPANN (Joint EV energy consumption and charging duration Training Prediction using Artificial Neural Networks), a novel jointly trainable artificial deep neural network framework for predicting stochastic EV user behavior by predicting their stay/charging duration and energy consumption simultaneously. We used a large-scale dataset of individual EV users’ charging transactions collected at a multi-site UCLA campus, the Los Angeles Department of Water and Power (LADWP), the Port of LA, the City of Santa Monica, and the City of Pasadena over a five-year period. This includes more than 50 EVSEs (Electric Vehicle Supply Equipment) and 216 EV charging points. Over 50,000 real-time transactions were recorded in our database with 341 distinct EV users. In this research, essential attributes of each charging session were identified, extracted from our database, and then fed into our JETPANN network. Using a jointly trained framework, our AI-enabled network predicts the stay/charging duration and the energy consumption of all the recorded EV users with an accuracy of above 99 percent. The proposed technique was tested and validated using all the collected historical charging data from individual EV users via realizing mean-absolute errors of training loss versus validation loss. Utilizing hyperparameter tuning and semi-grid search, the JETPANN with semi-optimized hyperparameters was achieved with the lowest mean-absolute errors of 0.927 and 0.068 in predicting stay/charging durations and energy consumptions, respectively, in a jointly trainable framework. This study demonstrates the potential of the JETPANN framework in the prediction of EV users’ behavior by using large-scale real-world data collected from a diverse pool of EV users, including faculty and students, occasional and frequent users, and early and late adopters.

Accurate Modeling of CCS Combo Type 1 Cable and Its Communication Performance Analysis for High-Speed EV-EVSE Charging System

This paper addresses the issue of electromagnetic interference (EMI) in electric vehicle supply equipment (EVSE) charging cables, which can disrupt the communication signal for the real-time monitoring of the charging status, leading to the termination of charging. We propose a dedicated measurement jig for the Combined Charging System Combo Type 1 (CCS-CT1) cable structure and models its electrical characteristics of the jig using the impedance peeling technique for de-embedding. The obtained pure S-parameters of CCS-CT1 are then used to conduct a simulation of the signal integrity problem caused by Gaussian noise, which is the worst-case scenario that can occur in a typical charging system. This paper suggests that the root cause of this problem may be related to the high-power AC/DC conversion device included in the EVSE, which uses a switch-mode power conversion (SMPC) method that involves nonlinear operation and can result in increased harmonic noise and a more complex signal protocol for precise control. Finally, this study provides insights into the challenges of implementing high-speed charging systems and offers a solution for obtaining the accurate electromagnetic characteristics of charging cables.

A Geo‐Spatial Method for Calculating BEV Charging Inconvenience using Publicly Available Data

AbstractAs governments and the automotive industry push towards electrification, it becomes increasingly critical to address the factors which influence individual car buying decisions. Evidence suggests that operational inconvenience or the perception thereof plays a large role in consumer decisions concerning Battery Electric Vehicles (BEVs). BEV ownership inconvenience and its causal factors have been relatively understudied, rendering efforts to mitigate the issues insufficiently informed. This paper presents a method of producing an empirical equation which relates operational inconvenience to a small number of housing and local Electric Vehicle Supply Equipment (EVSE) infrastructure factors. The paper then further provides a method of applying the equation in a geo‐spatial context allowing for the evaluation of the effects of policies in a geographical manner. this method enables future quantitative analyses concerning investment in EVSE infrastructure to be directly sensitive to BEV operational inconvenience due to charging.

Public charging infrastructure for EVs: A comprehensive analysis of charging patterns & real-world insights—Case study of Rabat City, Morocco

The introduction of electric vehicles (EVs) will contribute to decarbonizing our cities and make them more sustainable, which will help mitigate climate change. To ensure a successful transition to e-mobility, charging infrastructure development is considered an essential pillar. This paper presents a comprehensive analysis of electric vehicle supply equipment (EVSE) usage. It aims to understand charging patterns in urban environments and retrieve some real-world insights. By collecting a 2-years historical dataset (from 2019 to 2021) of a public EVSE based in Rabat, the methodology presented in this study covers time evolution assessment, energy delivery analysis, and users’ behavior characterization. The analysis of about 2835 events showed that the average energy delivery is 12 kWh corresponding to an average charging duration of 48 min. Also, more than 95% of charging sessions consumed less than 25 kWh, which corresponds on average to 60km. The performed analyses resulted in various findings that led to a set of recommendations that could be insightful for different types of stakeholders (policymakers, grid operators, charging operators, EV drivers, etc.). Furthermore, the methodology followed in this work is reproducible and may be used as a reference for other EVSE usage analyses.

Design and development of smart interoperable electric vehicle supply equipment for electric mobility

The transportation industry at present is moving towards electrification and the number of electric vehicles in the market increased with the different policies of the directorate. Consumers, who wish to contribute to green mobility are concerned about the limited availability of charging points due to high manufacturing costs and the interoperability issues related to smart charging. This work proposes an Internet of things-based low-cost, interoperable smart electric vehicle supply equipment for deploying in all charging stations. The device hardware is designed to monitor, analyze, and collect consumed energy by the vehicle and transfer this data to a connected network. The pre-defined messages associated with the firmware will help to record this data with a remote management server for further processing. The messages are defined in JavaScript Object Notation (JSON), which helps to overcome the interoperability issue. The device is smart because it can gather energy usage, detect device faults, and be intimate with the controller for a better operational environment. The associated management servers and mobile applications help to operate the smart device remotely and keep track of the usage statics. The developed low-cost, interoperable smart model is most suitable for two and three-wheeler vehicles.

EV Charging Station Using Solar Power

Abstract: The main requirement for the adoption and implementation of electric vehicles (EV) is the availability of charging facilities in public locations. In order to create an enabling EVSE ecosystem and hasten the adoption of EVs, this paper addresses several challenges relating to electric vehicle supply equipment (EVSE) or charging stations linked to legislation, standards, interoperability, and business models. Electric vehicle (EV) supply equipment (EVSE) or charging equipment are necessary for vehicle owners to adopt EVs. With varying degrees of success, several nations utilised various strategies and economic models to build the EVSE ecosystem. As India prepares to launch an EV revolution, a few crucial EVSE-related questions continue to plague the industry's players: What criteria apply to EVSE in India? whose will berun and keep up an EVSE? Utilities? Utility franchisees, perhaps? or outside parties like business owners, parking lot managers, and fleet operators? What will the electricity cost be for charging an EV? Will there be only energy charges or will there be capacity charges as well (minimum monthly fee per kW of capacity)? Who will cover the expense of the electric grid upgrade (higher capacity distribution transformers and new cables when necessary): the owner of the EVSE or the utility's usual grid improvement capex? Where will the public EVSEs be installed, and will the land be given away for free, at a discount, or at market value? This paper seeks to summarise the extensive work that has previously been done by numerous parties on the aforementioned topics and environment that will facilitate the rapid implementation of EVs.

A rough Dombi Bonferroni based approach for public charging station type selection

As the transition to electric mobility accelerates, charging infrastructure is rapidly expanding. Publicly accessible chargers, also known as electric vehicle supply equipment (EVSE), are critical not only for further promoting the transition but also for mitigating charger access anxiety among electric vehicle (EV) users. It is essential to install the proper EVSE configuration that meets the EV user’s various considerations. This study presents a multi-criteria decision-making (MCDM) framework for determining the best performing public EVSE type from multiple EV user perspectives. The proposed approach combines a new MCDM model with an optimal public charging station model. While the optimal model outputs are used to evaluate the quantitative criteria, the MCDM model assesses EV users’ evaluations of the qualitative criteria using nonlinear Bonferroni functions extended by rough Dombi norms. The proposed MCDM has standardization parameters with a flexible rough boundary interval, allowing for flexible and rational decision-making. The model is tested using real public EVSE charging data and EV users’ evaluations from the field. All public EVSE alternatives are studied. Among the five EVSE options, DCFC EVSE is found to be the best performing, whereas three-phase AC L2 is the least performing option. In terms of EV user preferences, the required charging time is found to have the highest degree of importance, while V2G capability is the least important. The comparative analysis with state-of-the-art MCDM methods validates the proposed model results. Finally, sensitivity analysis verified the ranking order.

EVKG: An interlinked and interoperable electric vehicle knowledge graph for smart transportation system

AbstractOver the past decade, the electric vehicle (EV) industry has experienced unprecedented growth and diversification, resulting in a complex ecosystem. To effectively manage this multifaceted field, we present an EV‐centric knowledge graph (EVKG) as a comprehensive, cross‐domain, extensible, and open geospatial knowledge management system. The EVKG encapsulates essential EV‐related knowledge, including EV adoption, EV supply equipment, and electricity transmission network, to support decision‐making related to EV technology development, infrastructure planning, and policy‐making by providing timely and accurate information and analysis. To enrich and contextualize the EVKG, we integrate the developed EV‐relevant ontology modules from existing well‐known knowledge graphs and ontologies. This integration enables interoperability with other knowledge graphs in the Linked Data Open Cloud, enhancing the EVKG's value as a knowledge hub for EV decision‐making. Using six competency questions, we demonstrate how the EVKG can be used to answer various types of EV‐related questions, providing critical insights into the EV ecosystem. Our EVKG provides an efficient and effective approach for managing the complex and diverse EV industry. By consolidating critical EV‐related knowledge into a single, easily accessible resource, the EVKG supports decision‐makers in making informed choices about EV technology development, infrastructure planning, and policy‐making. As a flexible and extensible platform, the EVKG is capable of accommodating a wide range of data sources, enabling it to evolve alongside the rapidly changing EV landscape.

Reliability Enhancement of Fast Charging Station under Electric Vehicle Supply Equipment Failures and Repairs

This work focuses on the enhancement of the charging reliability of both scheduled (SEVs) and opportunistic (UEVs) electric vehicle (EV) users in an EV fast charging station (FCS). The proposed charging coordination strategies allow UEVs to exploit unused charging resources to optimally utilize the limited charging resources of FCS. However, the optimum utilization of limited charging resources of an FCS while assuring a reliable charging process for plugged-in EVs under random failures of electric vehicle supply equipment (EVSE) is a real challenge for the FCS controller. When the FCS admits UEVs in addition to SEVs, assuring a satisfactory quality of service to both EV user categories is also dispensable. Therefore, we analyze the performance of reservation of off-board mobile chargers (MOBCs) to enhance the charging reliability of EV users while achieving high charging resource utilization. This work proposes resource allocation and charging coordination strategies for an FCS where MOBCs are used to enhance the charging reliability of both SEVs and UEVs. Moreover, the proposed dynamic charging resource coordination strategies are analyzed with a continuous time Markov-chain (CTMC). The presented results from the CTMC model demonstrate that the proposed strategies outperform the EV charging process of the FCS in terms of high resource utilization and reliability while guaranteeing a satisfactory quality of service to EV users.