Biblio

As a part of the global decarbonization agenda, the electrification of the transport sector involving the large-scale integration of electric vehicles (EV) constitues one of the key initiatives. However, the introduction of EV loads results in more variable electrical demand profiles and higher demand peaks, challenging power system balancing, voltage and network congestion management. In this paper, a novel optimal load scheduling approach for a coupled power and transportation network is proposed. It employs an EV charging demand forecasting model to generate the temporal-spatial distribution of the aggregate EV loads taking into account the uncertainties stemmed from the traffic condition. An AC optimal power flow (ACOPF) problem is formulated and solved to determine the scheduling decisions for the EVs, energy storage units as well as other types of flexible loads, taking into account their operational characteristics. Convex relaxation is performed to convert the original non-convex ACOPF problem to a second order conic program. Case studies demonstrate the effectiveness of the proposed scheduling strategy in accurately forecasting the EV load distribution as well as effectively alleviating the voltage deviation and network congestion in the distribution network through optimal load scheduling control decisions.

This paper addresses security and risk management of hardware and embedded systems across several applications. There are three companies involved in the research. First is an energy technology company that aims to leverage electric- vehicle batteries through vehicle to grid (V2G) services in order to provide energy storage for electric grids. Second is a defense contracting company that provides acquisition support for the DOD's conventional prompt global strike program (CPGS). These systems need protections in their production and supply chains, as well as throughout their system life cycles. Third is a company that deals with trust and security in advanced logistics systems generally. The rise of interconnected devices has led to growth in systems security issues such as privacy, authentication, and secure storage of data. A risk analysis via scenario-based preferences is aided by a literature review and industry experts. The analysis is divided into various sections of Criteria, Initiatives, C-I Assessment, Emergent Conditions (EC), Criteria-Scenario (C-S) relevance and EC Grouping. System success criteria, research initiatives, and risks to the system are compiled. In the C-I Assessment, a rating is assigned to signify the degree to which criteria are addressed by initiatives, including research and development, government programs, industry resources, security countermeasures, education and training, etc. To understand risks of emergent conditions, a list of Potential Scenarios is developed across innovations, environments, missions, populations and workforce behaviors, obsolescence, adversaries, etc. The C-S Relevance rates how the scenarios affect the relevance of the success criteria, including cost, schedule, security, return on investment, and cascading effects. The Emergent Condition Grouping (ECG) collates the emergent conditions with the scenarios. The generated results focus on ranking Initiatives based on their ability to negate the effects of Emergent Conditions, as well as producing a disruption score to compare a Potential Scenario's impacts to the ranking of Initiatives. The results presented in this paper are applicable to the testing and evaluation of security and risk for a variety of embedded smart devices and should be of interest to developers, owners, and operators of critical infrastructure systems.

Nowadays, with the continuous increase in oil prices and the worldwide shift towards clean energy, all-electric vehicles are booming. Thence, these vehicles need widespread charging systems operating securely and reliably. Consequently, these charging systems need the most robust cybersecurity measures and strong authentication mechanisms to protect its user. This paper presents a new security scheme leveraging human biometrics in terms of iris recognition to defend against multiple types of cyber-attacks such as fraudulent identities, man-in-the-middle attacks, or unauthorized access to electric vehicle charging stations. Fundamentally, the proposed scheme implements a security mechanism based on the inherently unique characteristics of human eye biometric. The objective of the proposed scheme is to enhance the security of electric vehicle charging stations by using a low-cost and efficient authentication using k-Nearest Neighbours (KNN), which is a lightweight encryption algorithm.We tested our system on high-quality images obtained from the standard IITD iris database to search over the encrypted database and authenticate a legitimate user. The results showed that our proposed technique had minimal communication and computation overhead, which is quite suitable for the resource-limited charging station devices. Furthermore, we proved that our scheme outperforms other existing techniques.

The Supervisory control and data acquisition (SCADA) systems have been continuously leveraging the evolution of network architecture, communication protocols, next-generation communication techniques (5G, 6G, Wi-Fi 6), and the internet of things (IoT). However, SCADA system has become the most profitable and alluring target for ransomware attackers. This paper proposes the deep learning-based novel ransomware detection framework in the SCADA controlled electric vehicle charging station (EVCS) with the performance analysis of three deep learning algorithms, namely deep neural network (DNN), 1D convolution neural network (CNN), and long short-term memory (LSTM) recurrent neural network. All three-deep learning-based simulated frameworks achieve around 97% average accuracy (ACC), more than 98% of the average area under the curve (AUC) and an average F1-score under 10-fold stratified cross-validation with an average false alarm rate (FAR) less than 1.88%. Ransomware driven distributed denial of service (DDoS) attack tends to shift the state of charge (SOC) profile by exceeding the SOC control thresholds. Also, ransomware driven false data injection (FDI) attack has the potential to damage the entire BES or physical system by manipulating the SOC control thresholds. It's a design choice and optimization issue that a deep learning algorithm can deploy based on the tradeoffs between performance metrics.

In this paper we propose a cryptographic solution that provides non-repudiation and end-to-end security for the electric-vehicle-charging ecosystem as it exists in the Netherlands. It is designed to provide long-term non-repudiation, while allowing for data deletion in order to comply with the GDPR. To achieve this, we use signatures on hashes of individual data fields instead of on the combination of fields directly, and we use Merkle authentication trees to reduce the overhead involved.

In this article, to deal with data security requirements of electric vehicle users, a key management scheme for smart charging has been studied. According to the characteristics of the network, three elements and a two-subnetwork model between the charging and the electric vehicle users have been designed. Based on the hypergraph theory, the hypergraph structure of the smart charging network is proposed. And the key management scheme SCHKM is designed to satisfy the operational and security requirements of this structure. The efficiency of SCHKM scheme is analyzed from the cost experiment of key generation and key storage. The experimental results show that compared with the LKH, OFT and GKMP, the proposed key management scheme has obvious advantages in multi-user and key generation cost.

This paper proposes a multi-modular AC-DC converter system using wireless communication for a rapid charger of electric vehicles (EVs). The multi-modular topology, which consists of multiple modules, has an advantage on the expandability regarding voltage and power. In the proposed system, the input current and output voltage are controlled by each decentralized controller, which wirelessly communicates to the main controller, on each module. Thus, high-speed communication between the main and modules is not required. As the results in a reduced number of signal lines. The fundamental effectiveness of the proposed system is verified with a 3-kW prototype. In the experimented results, the input current imbalance rate is reduced from 49.4% to 0.1%, where total harmonic distortion is less than 3%.