Electric Vehicle Supply Equipment Research Articles

Applying Deep Learning-based concepts for the detection of device misconfigurations in power systems

The electrical energy system is undergoing major changes due to the necessity for more sustainable energy generation and the following increased integration of novel grid-connected devices, such as inverters or electric vehicle supply equipment. To operate reliably in novel circumstances, as created by the decentralization of generation, power systems usually need grid supportive functions provided by these devices. These functions include control mechanisms such as reactive power dispatch used for voltage control or active power reduction depending on the voltage. As the main contribution of this work, an approach for the development of the detection of misconfigured (e.g., wrongly parameterized control curve) grid devices using solely operational data is proposed. By generating and analyzing operational data of power distribution grids, a Deep Learning-based approach is applied to the detection problem given. An end-to-end framework is used to synthesize and process the data as well as to apply machine learning techniques to it. The results offer insights into the applicability and possible ways to improve the proposed solution and how it could be employed by grid operators. The findings show that DL methods, in contrast to traditional machine learning, can be used for the problem at hand and that the framework developed offers the necessary tools to fine-tune and scale the solution for broader usage.

Research on the design and verification of the charging compatibility for electric vehicle ChaoJi charging

The international mainstream DC charging systems CHAdeMO 2.0 in Japan, GB/T 2015 in China, CCS1/CCS2 in Europe, and the U.S. have different control pilot circuit and the coupler designs. Control pilot circuit is an important component of the electric vehicle (EV) charging system and is used to monitor the function of the interaction between the EV and the electric vehicle supply equipment (EVSE). Existing system designs can make the difficult to achieve the compatibility between different charging systems, and reduce the user experience. This study first analyzes the practical applications of four existing mainstream DC charging systems. There are certain defects in structural design, safety, compatibility, and control pilot function, the introduction of a new generation of ChaoJi charging technology will change all these. Meanwhile the vehicle adapter design solution can perfectly solve the problem of physical compatibility between the existing charging system and ChaoJi vehicle. In addition, a new control pilot circuit design is proposed, to identify normal charging and abnormal states by hard node signals and states switches. By building a test environment, this new control pilot circuit has proven to its excellent characteristics in terms of fault monitoring, backward compatibility, and future-proof applications. ChaoJi charging technology has been adopted by CHAdeMO 3.0, laying the foundation for a globally harmonized charging system.

Lithium-Ion Cell Characterization, Using Hybrid Current Pulses, for Subsequent Battery Simulation in Mobility Applications

In this paper, a characterization method for a lithium iron phosphate (LFP) pouch cell is presented and evaluated, using a method that applies to hybrid current pulses called hybrid power pulse characterization (HPPC). The purpose of the study is to validate the developed mathematical model capable of offering good results for virtualization of the cell with extrapolation capability for the entire battery. This type of characterization was tested before but on cells with low capacity where relatively small currents were applied. Here, the model is intended to be used for the development of electrical mobility applications, such as electric vehicles (EV) and electric vehicle supply equipment (EVSE), where high capacity and currents are required through the cell. The comparison between the real and simulated cell was made with two sets of results obtained from HPPC and using the FTP-72 speed profile by emulating real current conditions, where both show that the method is reliable under the tested conditions and can be used for the considered application.

An overview of bidirectional electric vehicles charging system as a Vehicle to Anything (V2X) under Cyber–Physical Power System (CPPS)

Nowadays, EVs are rapidly increasing in popularity, and are accepted as the vehicles of the future all over the world. The most important components are their battery and charging systems. The energy capacity of EVs’ batteries has a significant potential to supply different energy requirements. Therefore, EVs must be designed in accordance with bidirectional power flow, and Electric Vehicle Supply Equipment (EVSE) should be upgraded as Electric Vehicle Power Exchange Equipment (EVPE). This power exchange infrastructure can be called Vehicle-to-Anything (V2X). V2X will also be the key solution for energy grids of the future that will turn into a much larger and smarter system with the help of emerging digitalization technologies, such as Artificial Intelligence (AI), Distributed Ledger Technology (DLT), and the Internet of Things (IoT). This study introduces a multi-layer Cyber–Physical Power Systems (CPPS) framework to explore the potential of V2X technologies allowing bidirectional charging. In addition, the impact of e-mobility is discussed from the V2X perspective. V2X has the potential to provide more practical use of electric vehicles and to bring advantages to the user in terms of both economy and comfort, thus accelerating the transformation of e-mobility and making it easier to accept.

Accelerating electric vehicle adoption: techno-economic assessment to modify existing fuel stations with fast charging infrastructure

With the increasing electric vehicle (EV) penetration, there arises an immediate need for charging infrastructure. In the future, the electrification of transportation will reduce the requirement of existing fuel stations, thereby rendering them obsolete. However, they are best suited to cater to the charging demand of EVs as the drivers are accustomed to the locations and the incremental cost of providing this service will be lower. In this paper, we propose a novel methodology to assess the techno-economic feasibility of retrofitting an existing fuel station with EV charging infrastructure also known as Electric Vehicle Supply Equipment (EVSE). To further enhance the value proposition, the potential of integrating Battery Energy Storage System (BESS) with EV charging infrastructure, which results in the reduction of grid connection costs, is studied. The sustainability of the proposed system is improved with additional onsite Photovoltaic (PV) generation. The proposed methodology is implemented for the UK as a case study. The configurations in this study are designed based on the technical considerations involved in retrofitting a typical fuel station as a fast charging facility for EVs. From the results, it is observed that the configurations with 4 EVSE, 1 BESS, and 8 h of operation and the configuration with 4 EVSE, 1 BESS, and 1 PV system for 8 h of operation are economically viable. The abovementioned configurations are the most economically feasible configurations in terms of the Net Present Value (NPV), Internal Rate of Return (IRR) and the Discounted Payback Period (DPP) amongst the other configurations considered in this study. The proposed methodology indicates that though the connection cost is the dominant factor affecting the feasibility, the use of BESS with or without PV can reduce the connection cost by almost 90% depending on the capacity of BESS. The methodology acts as a decision support tool to select a techno-economically feasible configuration of EVSE, BESS, and PV.Graphical abstract

Optimization of Waiting Time for Electric Vehicles Using a Fuzzy Inference System

Electric vehicles (EVs) need to be recharged at intermediate locations, such as shopping malls, restaurants, and parking lots, to meet the daily commute requirements of their users. Currently, public electric vehicle supply equipment (EVSE) serve EVs by conventional methods, which can result in long waiting time for users. This issue reduces the travel efficiency of EVs and thus affects user comfort. Most previous research has studied energy consumption and charging cost optimization; however, comparatively less work has focused on waiting time optimization despite its great importance from the EV user’s perspective. In this paper, we formulate the waiting time optimization as a fuzzy integer linear programming problem and propose a novel heuristic fuzzy inference system-based algorithm (FISA) that resolves the objective function and minimizes the waiting time of EVs at public EVSE installations. We developed the underlying fuzzy inference system by defining the membership functions, expert rules, and formulation for obtaining the optimal solution. The novel FISA automates the correlations of the uncertain and independent input parameters into weighted control variables and resolves the objective function in each sampling period to optimize the waiting time for EVs with the most urgent service requirements. A java language-based simulator is developed for a parking lot to evaluate the effectiveness of the proposed FISA. The simulation results indicate higher efficiency of the proposed FISA compared with state-of-art scheduling algorithms.

An Electric Vehicle Battery-Swapping System: Concept, Architectures, and Implementations

Over the years, electric vehicles (EVs) have gained extensive attention and popularity worldwide. The ownership of EVs has been rapidly growing globally. The increasing EV charging (EVC) demand creates growth of electric energy demand and imposes additional stress on the power grid. To meet the required EVC demand, it is necessary to construct sufficient EV supply equipment (EVSE). The construction of EVSE is a key prerequisite for the wider deployment of EVs. Although EVC stations (CSs) have gained a default position for EV infrastructure, battery-swapping systems (BSSs) have also drawn considerable attention. In this article, we first introduce the system architectural design of BSSs. Then we present four kinds of BSS designs. The lessons learned from the practice of BSS implementation and operation in China are provided. BSS standardization is a determining issue in BSS development. State-of-the-art BSS standardization development is summarized, while key technical challenges and opportunities of BSSs are listed.

Risk Adversarial Learning System for Connected and Autonomous Vehicle Charging

In this article, the design of a rational decision support system (RDSS) for a connected and autonomous vehicle charging infrastructure (CAV-CI) is studied. In the considered CAV-CI, the distribution system operator (DSO) deploys electric vehicle supply equipment (EVSE) to provide an electrical vehicle (EV) charging facility for human-driven connected vehicles (CVs) and AVs. The charging request by the human-driven EV becomes irrational when it demands more energy and charging period than its actual need. Therefore, the scheduling policy of each EVSE must be adaptively accumulated the irrational charging request to satisfy the charging demand of both CVs and autonomous vehicles (AVs). To tackle this, we formulate an RDSS problem for the DSO, where the objective is to maximize the charging capacity utilization by satisfying the laxity risk of the DSO. Thus, we devise a rational reward maximization problem to adapt the irrational behavior by CVs in a data-informed manner. We propose a novel risk adversarial multiagent learning system (RAMALS) for CAV-CI to solve the formulated RDSS problem. In RAMALS, the DSO acts as a centralized risk adversarial agent (RAA) for informing the laxity risk to each EVSE. Subsequently, each EVSE plays the role of a self-learner agent to adaptively schedule its own EV sessions by coping advice from RAA. The experiment results show that the proposed RAMALS affords around 46.6% improvement in charging rate, about 28.6% improvement in the EVSE’s active charging time, and at least 33.3% more energy utilization, as compared to a currently deployed ACN EVSE system, and other baselines.

A novel ensembling of deep learning based intrusion detection system and scroll chaotic countermeasures for electric vehicle charging system

The usage of Electric vehicle (EVs) has been exponentially growing due to its focus on eco-friendly means of transport, distributed charging platform and user dictated supporting infrastructures. The EVs are charged by the charging stations which equipped with Electric Vehicle Supply Equipment (EVSE) that contains Internet enabled computers. These systems are considered to be more important for controlling the function such as charging electric vehicles, authorization and smart connection to the local power grid using different wireless technologies such as green WIFI, Bluetooth and even 5 G. The cyber-attacks such as DoS and DDoS attacks can violate integrity, confidentiality and availability of the EVSE resources. Hence the intelligent Intrusion Detection System (IDS) is required to ensure the system for the robust and trustworthy deployment of EVSE resources. To meet the above challenge, this paper proposes new composite and intelligent system which contains the deep learning based IDS and high random chaotic generators to safeguard the data against the different cyber-attacks. The proposed IDS has been modelled based on Gated Recurrent Units (GRU) and counter measures are performed by adopting the Enhanced Chaotic Scroll attractor keys (ECSA). The contribution of this research paper is as follows: Novel Dataset Preparation for EVSE under different attack scenarios, Implementation of high accurate multi-objective accurate GRU based IDSs, Design of Enhanced Chaotic Countermeasure Encryption Schemes for the counterfeiting the attacks in Internet Enabled EVSE system. The extensive experimentation has been carried out into two important phases. In first phase algorithm centric metrics such as prediction accuracy, time of detection, whereas in second phase key centric metrics such as Number of Changing Pixel Rate (NPCR), Unified Averaged Changed Intensity (UACI), Key sensitivity and entropy are calculated and compared with the other existing methodologies. Results demonstrates that the proposed ensemble system has outperformed than the other methodologies and proves its strong place in designing the more secured Internet Enabled EVSE systems.

An activity-based travel and charging behavior model for simulating battery electric vehicle charging demand

The expansion of the battery electric vehicle (BEV) market requires considerable changes in the supply of electricity to fulfill the charging demand. To this end, understanding the spatio-temporal distribution of BEV charging demand at a micro level is crucial for optimal electric vehicle supply equipment (EVSE) planning and electricity load management. This research proposes an integrated activity-based BEV charging demand simulation model, which considers both realistic travel and charging behaviors and provides high-resolution spatio-temporal demand in real-world applications. Moreover, a novel charging choice model is proposed which provides more realistic demand modeling by allowing critical non-linearities in random utility to better describe observed charging behaviors. The results of a case study for the Atlanta metropolitan area imply that work/public charging has a substantial potential market, which can comprise up to 64.5% of the total demand. Out of multiple charging modes, demand for direct-current fast charging (DCFC) is prominent at work/public locations, and it makes up the largest portion of the non-residential demand in all simulation scenarios. Moreover, charging behaviors have significant impacts on the demand distribution. Peak power demand for use of level-2 chargers is 49% to 91% higher among high-risk-sensitive users than among risk-neutral users. Users’ preferences for fast charging rates can change the DCFC demand from 36.4% of the total demand to 53.7% of the total. This study helps to qualitatively analyze the factors that figure in charging demand and their impacts on the demand distribution. The results can be directly used in EVSE planning and electricity load prediction.

Stochastic Modeling and Analysis of Public Electric Vehicle Fleet Charging Station Operations

The electric vehicle (EV) fleet is gradually growing into a major part of public transportation. Proper planning and operation of EV supply equipment (EVSE) is essential to ensure the efficient and economic operations of the EV fleets. Charging stations (CS) have gained market attention due to their lower cost and versatility. Battery swapping stations (BSS) have also received considerable attention because of their promise to provide fast and sustainable battery replacements. However, their commercial viability is unclear due to their requirement for large capital and infrastructure deployment. In this paper, we develop a stochastic model for interactions between CS/BSS and taxi/bus fleets. The model is based on a realistic abstraction of users’ behavior defined by various stochastic processes. It also considers the dynamic impacts of the road congestion. Analytical revenue boundaries are derived and verified by simulations. These simulation results may prove valuable for future studies of public transit.

Estimating Potential Employment Impact of the Charging Infrastructure used to Support Transportation Electrification in the United States

Increased concern over greenhouse gas (GHG) emissions and climate change is encouraging many states, companies, and consumers to focus on zero emission vehicle technologies such as electric vehicles (EVs). A major barrier to widespread EV adoption, however, is range anxiety as there is currently insufficient electric vehicle supply equipment (EVSE) available. Although not the primary goal primary of EVSE installations, one of their side effects and a goal of President Biden’s infrastructure plans is their impact on employment, both initially as stations are developed and activated and over time and as they continue to provide charging to a growing population of EVs. This study estimates the potential employment effects of the deployment and operation of President Biden’s goal of installing 500,000 charger plugs. To do this, we develop an input-output (IO) based model called JOBS EV. Unlike existing analyses, JOBS EV includes both the employment effects caused by the front-of-meter EVSE equipment needed at a particular site and the back-of-meter or upstream equipment used to obtain power from the existing utility’s distribution network. The model results indicate that approximately 1.1 million new jobs will be created over a 10-year period. The modeling process outlined in this paper, in addition to the results presented, may be useful to stakeholders involved in transportation decarbonization efforts as another means of evaluating the costs and benefits of pursuing electrification. Further work is needed to improve the underlying IO model to better account for nascent industries, to accurately calculate local share percentages, and to capture the employment effects of the complete EVSE life cycle.

Spatial and temporal patterns of electric vehicle charging station utilization: a nationwide case study of Switzerland

Abstract The expansion of the public charging infrastructure for electric vehicles is seen as central to the development of electric mobility in many countries. Although national studies of charging infrastructure utilization based on real-world data would be a sound basis for demand planning, such studies are scarce. Using Switzerland as an example, this study examines the spatial and temporal patterns of charging infrastructure utilization. To this end, detailed, nationwide, real-time utilization data from 3086 electric vehicle supply equipment units (EVSEs) at electric vehicle charging stations were collected over a period of several months and analyzed exploratively and statistically. The maximum average utilization rate of the EVSEs surveyed during the study period is between 14% and 16%, depending on the day of the week and time of day. Most charging occurs Monday through Friday during peak working hours and on Saturday during the day. The median utilization time is higher in the largest cities than the statewide average. Charging stations along major transit routes do not have higher utilization rates than in other locations. The results suggest that public charging infrastructure is used primarily in cities and agglomeration during work hours. The findings from this study may help plan and make better use of funding to expand charging infrastructure.

Review of Electric Vehicle Charger Cybersecurity Vulnerabilities, Potential Impacts, and Defenses

Worldwide growth in electric vehicle use is prompting new installations of private and public electric vehicle supply equipment (EVSE). EVSE devices support the electrification of the transportation industry but also represent a linchpin for power systems and transportation infrastructures. Cybersecurity researchers have recently identified several vulnerabilities that exist in EVSE devices, communications to electric vehicles (EVs), and upstream services, such as EVSE vendor cloud services, third party systems, and grid operators. The potential impact of attacks on these systems stretches from localized, relatively minor effects to long-term national disruptions. Fortunately, there is a strong and expanding collection of information technology (IT) and operational technology (OT) cybersecurity best practices that may be applied to the EVSE environment to secure this equipment. In this paper, we survey publicly disclosed EVSE vulnerabilities, the impact of EV charger cyberattacks, and proposed security protections for EV charging technologies.

A multi-objective optimization model for EVSE deployment at workplaces with smart charging strategies and scheduling policies

This study proposes a multi-objective optimization model to determine the optimal charging infrastructure for a transition to plug-in electric vehicles (PEVs) at workplaces. The developed model considers all cost aspects of a workplace charging station, i.e., daily levelized electric vehicle supply equipment (EVSE) infrastructure cost, PEV energy and demand charges. These single-objective functions are aggregated in a multi-objective optimization framework to find the Pareto optimal solutions. Smart charging strategies with interrupted and uninterrupted power profiles are proposed to maximize the use of EVSE units. The charging behavior model is developed based on collected workplace charging data. The model is tested with various scheduling policies to investigate their impact on the behaviors of EVSE types from different perspectives. Finally, a sensitivity analysis is performed to assess the impacts of battery sizes and onboard charger ratings on cost behavior. It is shown that the proposed model can achieve up to 7.8% and 14.6% cost savings as compared to single-objective optimal models and the current charging practice, respectively. The unit cost is found to be more sensitive to scheduling policies than the charging strategies. It is also found that the flexibility ratio policy gives the best PEV scheduling with the lowest unit cost and the most efficient use of the grid assets.

Supraharmonic and Harmonic Emissions of a Bi-Directional V2G Electric Vehicle Charging Station and Their Impact to the Grid Impedance

Bidirectional electric vehicle supply equipment and charging stations (EVSE) offer new business models and can provide services to the electrical grid. The smart grid lab in Vienna gives unique testing possibilities of future smart grids, as different type of electrical equipment can be operated at a reconstructed, well-known distribution grid. In this work the harmonic and supraharmonic emissions of a bidirectional EVSE are measured according to IEC61000-4-7 and IEC61000-4-30 Ed3 standard as well as the high-frequency grid impedance. In addition, the efficiency and the power factor are determined at various operating points. Although THDi at nominal power (10 kW) is very low and the efficiency and power factor is very high, at low power levels the opposite situation arise. Supraharmonic emissions remain stable independent of the charging/discharging power, and both wideband and narrowband emissions occur. The additional capacitance when connecting the EVSE impacts the high-frequency grid impedance substantially and generates resonance points.

Co-Simulation of Optimal EVSE and Techno-Economic System Design Models for Electrified Fleets

As the transition to electric mobility is expanding at a rapid pace, operationally feasible and economically viable charging infrastructure is needed to support electrified fleets. This paper presents a co-simulation of optimal electric vehicle supply equipment (EVSE) and techno-economic system design models to investigate the behaviors of various EVSE configurations from cost and technical aspects. While the system design optimization is performed for a grid-tied PV system, the optimal EVSE model considers all EVSE options that are currently installed at workplaces. To investigate the impact of the EV utilization rate, three fleet sizes are considered, which are generated based on real EV fleet data. Furthermore, the impact of electricity rates is also explored through an innovative business EV-specific (BEV) rate and a conventional time-of-use (ToU) tariff. It is shown that investing in grid-tied renewable energy technologies for workplace charging infrastructure supply can lower charging costs. Cost savings differ from EVSE types and fleet size under the BEV rate, while EVSEs display similar cost-saving behavior under the ToU tariff irrespective of fleet size. DC Fast Charging (DCFC) EVSE is found to be highly sensitive to fleet size as compared to AC EVSEs. Moreover, DCFCs make better use of the BEV rate, which makes their economics competitive as much as AC EVSEs. Finally, it is found that the fleet size and AC EVSE types have a minor effect on the use of renewable energy in contrast to the DCFC case.

Experimental Implementation of a Wireless Communication System for Electric Vehicle WPT Charger

Electric Vehicle (EV) market is growing rapidly and the demand for new charging methods is up surging. In order to meet this growing demand, wireless power transmission is a good choice for providing energy because it has many advantages such as convenience, user-friendliness and safety. Such charging method requires a wireless communication between EVs and electric vehicle supply equipment (EVSE) to ensure that power is transferred in a pre-cisely controlled way. This paper proposes a wireless communication system based on Raspberry Pi 4 and TMS320F28335 development kit from Texas In-struments. It consists in acquisition and wireless transmission of analogue data from a client to a server. The system is experimentally verified and the obtained result shows that the system provides a good performance for a range of fre-quencies. Thus, the proposed solution presents a good basis for the next step of controlling wireless power transmission.

Electric Vehicle Supply Equipment for Level-2 Charging

Electric Vehicle Supply Equipment for Level-2 Charging

Design and Experimental Validation of Power Electric Vehicle Emulator for Testing Electric Vehicle Supply Equipment (EVSE) with Vehicle-to-Grid (V2G) Capability

Nowadays, the global decarbonization and electrification of the world’s energy demands have led to the quick adoption of Electric Vehicle (EV) technology. Therefore, there is an urgent need to provide a wide network of fast Vehicle-to-Grid (V2G) charging stations to support the forecast demand and to enable enough autonomy of such devices. Accordingly, V2G charging stations must be prepared to work properly with every manufacturer and to provide reliable designs and validation processes. In this way, the development of power electric vehicle emulators with V2G capability is critical to enable such development. The paper presents a complete design of a power electric vehicle emulator, as well as an experimental testbench to validate the behaviour of the proposal.