Timeout notification method for efficient V2G communication

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.

The penetration and spread of cyberattacks can vary depending on the structure and nature of each infrastructure. Among the infrastructures that have not been sufficiently studied and are vulnerable to cyberattacks is the electric vehicle infrastructure (EVI). This infrastructure is an ideal target for such attacks for many reasons, including the mobile nature of electric vehicles (EVs), high load demand, technologies, and communication networks of EVs. If EVs, as the moving part of smart grids, become compromised due to a cyberattack, they can spread this attack to other components of the infrastructure, such as EVs supply equipment (EVSE), which is used for recharging in a short period of time. So, without any defensive to attacks, it may spread to other parts and infrastructures of the smart grid. In this chapter, a pervasive cyberattack on EVI and propagation of the attack have been studied in three parts including EV charging infrastructure in EVSEs, EVSEs, and EVs shared communication network, considering the compromising probabilities or threat levels of EVs and EVSEs. As a result, it minimizes the maximum threat level and reduces the process of spreading the cyberattack in both EVI and also the smart grid.KeywordsSmart gridElectric vehicleCyberattacksCommunication networkAttack propagationResponse model

Policymakers regularly intensify efforts to increase electric vehicle (EV) adoption to reduce greenhouse gas (GHG) emissions caused by the transportation sector. Despite various incentives, EV adoption has certain drawbacks, including limited ranges, long charging times, and sparse electric vehicle supply equipment (EVSE) infrastructure. Access to EVs is often a privilege for specific communities, causing inequity in adoption and access to public charging infrastructure. To enable a user-centered and equitable deployment of EVSE infrastructure, a detailed understanding of EV charging behavior and its different facets is crucial. This research establishes a holistic approach to understanding user behavior using real-world charging data collected at various public and residential EVSE networks. Throughout this work, multivariate charging session data is analyzed for EV user charging behavior using various statistical methods, advanced algorithms, and time series analysis. This novel and holistic approach to understanding charging station utilization offers new insights into EVSE network service level requirements and explains the relationship between charging behavior, urbanicity, and locale. The findings of this work indicate that significant differences in charging behavior between charging networks exist. An urban-rural divide in charging station utilization is identified, which is inconsistent throughout different areas and regions. Users from rural areas charge further away from their homes than urban users, indicating a lack of EVSE in rural areas and supporting the existence of an urban-rural divide in EVSE access. Charging behavior is further found to differ significantly between provinces, especially regarding intensity and etiquette. The results show that longer charging times usually concur with lower charging frequencies, reducing the service level and profitability of EVSE. Residential and public charging demand follows an inverse daily pattern, represented by a bi-daily switch in peak station utilization. While public charging demand times are concentrated during the day, residential EVSE is mainly used in the evening and throughout the night. The switch in charging demand proposed the “EV Duck Curve” and suggests that residential charging behavior can increase the renewable energy “Duck Curve” issue, which may be further amplified by a future increase in public direct current fast charger deployment. This work aims to demonstrate the differences in human charging behavior in various contexts by applying robust and multifaceted methods. Current EV charging behavior research lacks a holistic approach that incorporates various types and locations of charging networks when exploring user behavior. This research suggests that findings on charging behavioral patterns in one network are not transferable

<div class="section abstract"><div class="htmlview paragraph">The added connectivity and transmission of personal and payment information in electric vehicle (EV) charging technology creates larger attack surfaces and incentives for malicious hackers to act. As EV charging stations are a major and direct user interface in the charging infrastructure, ensuring cybersecurity of the personal and private data transmitted to and from chargers is a key component to the overall security. Researchers at Southwest Research Institute® (SwRI®) evaluated the security of direct current fast charging (DCFC) EV supply equipment (EVSE). Identified vulnerabilities included values such as the MAC addresses of both the EV and EVSE, either sent in plaintext or encrypted with a known algorithm. These values allowed for reprogramming of non-volatile memory of power-line communication (PLC) devices as well as the EV’s parameter information block (PIB). Discovering these values allowed the researchers to access the IPv6 layer on the connection between the EV and EVSE and use traditional ethernet penetration testing methods, including port and vulnerability scanning. Port scanning exposed open SSH and HTTP services, the latter of which was vulnerable and allowed unauthenticated retrieval of proprietary information. The ports should be secured, or closed if unneeded, to prevent this type of vulnerability.</div></div>

In pursuit of a more sustainable transportation system, electric vehicles (EVs) have the potential to play a fundamental role due to their improved efficiency and lower emissions. The absence of an adequate electric vehicle supply equipment (EVSE) network has been one of the major obstacles for the mass adoption of EV, in large municipalities of developing countries. This is the case in Belo Horizonte (BH), Brazil, which also has a high motorization rate (7 light-duty vehicles per 10 inhabitants). The purpose of this study is to measure and identify the optimal locations for EVSE according to selected criteria to meet the needs of light-duty electric vehicles (LDEV) corresponding to a penetration of 1% by 2025 in the municipality of BH. The study highlights the most important attributes that need to be considered for the installation of an EVSE network in an urban space for a developing country. Multi-Criteria Decision Making (MCDM), the Weighted Linear Combination (WLC) method, and the Analytical Hierarchy Process (AHP) technique based on the inputs from a group of Brazilian electrical mobility specialists, coupled with a Geographic Information System (GIS) modeling tool, were used for this study. The results revealed that around 1,200 EVSE units are needed, with a large concentration of EVSE in a small region. We also illustrate where stakeholders should focus their attention for the successful promotion of EV. The development methodology has the potential to be applied in other future EVSE development projects.

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.

Open Charge Point Protocol (OCPP) 1.6 is widely used in the electric vehicle (EV) charging industry to communicate between Charging System Management Services (CSMSs) and Electric Vehicle Supply Equipment (EVSE). Unlike OCPP 2.0.1, OCPP 1.6 uses unencrypted websocket communications to exchange information between EVSE devices and an on-premise or cloud-based CSMS. In this work, we demonstrate two machine-in-the-middle attacks on OCPP sessions to terminate charging sessions and gain root access to the EVSE equipment via remote code execution. Second, we demonstrate a malicious firmware update with a code injection payload to compromise an EVSE. Lastly, we demonstrate two methods to prevent availability of the EVSE or CSMS. One of these, originally reported by SaiFlow, prevents traffic to legitimate EVSE equipment using a DoS-like attack on CSMSs by repeatedly connecting and authenticating several CPs with the same identities as the legitimate CP. These vulnerabilities were demonstrated with proof-of-concept exploits in a virtualized Cyber Range at Wright State University and/or with a 350 kW Direct Current Fast Charger at Idaho National Laboratory. The team found that OCPP 1.6 could be protected from these attacks by adding secure shell tunnels to the protocol, if upgrading to OCPP 2.0.1 was not an option.

This paper describes “smart charging” systems for plug-in hybrid electric vehicles (PHEVs). The principal design feature is that the system uses gathered information to adaptively control PHEV charging, and does so in a way that allows customer PHEVs to still be charged at a preferred rate (cost). This paper reviews the drivers for smart charging, including electric grid readiness for large adoption rates of PHEVs, and considers national, regional and local distribution level issues. At the distribution level, the effect of increased PHEV charging loads on transformers is considered. The current state of standardization is reviewed with emphasis on communication messages and use cases that reflect smart charging attributes. Centralized system approaches are described, such as integrating electric vehicle supply equipment (EVSE), i.e. chargers, into Advanced Metering Infrastructure (AMI) networks, and treating EVSEs as controllable loads for Demand Response programs. Metering and monitoring the transformers that feed EVSEs can drive a control scheme that is either centralized or distributed. Alternatives to AMI-integration for centralized networks are also reviewed, including commercially available systems. Additionally, smart charging is considered from the billing perspective, where system approaches are described that allow for identification and association between connected PHEVs, EVSEs, premise meters and other smart devices.

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 which are currently installed at workplaces. To investigate the impact of EV utilization rate, three fleet sizes are considered that are generated based on real EV fleet data. Furthermore, the impact of electricity rates is also explored through an innovative 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.

Evaluation of charging infrastructure requirements and operating costs for plug-in electric vehicles

Detection and isolation of battery charging cyberattacks via Koopman operator

Plug-in electric vehicles (PEVs), that is, vehicles that use electric motors as their primary means of power, began to be mass-produced in 2011. The need for electric vehicle charging stations [known as electric vehicle supply equipment (EVSE)] in industrial and commercial facilities is growing and will continue to grow as the number of PEVs increases. Power system engineers will face many challenges when applying the EVSEs to their facility power systems. As there are relatively few EVSEs in use today, the issues in the application of this equipment have yet to be seen. The purpose of this article is to make the reader more familiar with EVSEs by identifying and discussing the standards to which they are designed and built and describing the common features and options that are now available.

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.

With an increase in Electric Vehicles (EV) that have unique charging requirements, finding suitable Electric Vehicle Supply Equipment (EVSE) for charging EVs is becoming a difficult and challenging task. This paper focuses on developing a cloud-based and customer-oriented EV charging station recommender, which suggests EV charging stations for users based on their location and requirements. The recommender will take the location of the vehicle and other user needs such as type of connector, accessibility, access time, parking lot, station category etc. as inputs to recommend the most convenient charging stations in increasing order of distance from the user's location and facilitate slot booking. From the charging station dataset, the Random Forest Algorithm (RFA) is applied for finding stations that are near the vehicle location; Linear Search Algorithm (LSA) for filtering stations that satisfy the user requirements; and the Haversine formula for calculating distance. Streamlit is used for developing an end-user cloud application that is deployed in Streamlit Cloud. Additional features, such as charging request scheduling, and using live locations to update the station list, can be integrated to the system in the future.

The key challenge with the rapid proliferation of electric vehicles (EVs) is to optimally manage the available energy charging resources at EV fast-charging stations (FCSs). Furthermore, the rapid deployment of fast-charging stations provides a viable solution to the potential driving range anxiety and charging autonomy. Costly grid reinforcements due to extra load caused by fast charging can be omitted using a dedicated energy storage and/or renewable energy system at the FCS. The energy supply and fixed number of EV supply equipment (EVSE) are considered as the limited charging resources of FCS. Amidst various uncertainties associated with the EV charging process, how to optimally utilize limited charging resources with opportunistic ultra-fast charging EV users (UEVs) is studied in this work. This work proposes resource allocation and charging coordination strategies that facilitate UEVs to dynamically exploit these limited charging resources with defined liabilities when pre-scheduled users (SEVs) do not occupy them to utilize limited charging resources maximally. Moreover, the proposed dynamic charging coordination strategies are analyzed with a Monte Carlo simulation (MCS). The presented numerical results reveal that the major drawbacks of under-utilization of limited charging resources by SEVs can be significantly improved through dynamic charging resource allocation and coordination along with UEVs. With the proposed charging coordination strategies in this study, the maximum charging resource utilization of considered FCS with 10 EVSE has been improved to 90%, which bounds to 78% only with SEVs.