The rapid advancement in electric vehicle (EV) technology stimulated the development of fast chargers, with innovative solutions in power electronics (PEs), communication, protection, and control. This literature review explores the evolving landscape of fast EV charging infrastructures, and focuses on their topologies, advanced PEs solutions, and associated challenges. In addition, this article explores the multifaceted aspects of fast charging systems. In addition, the article discusses the protection systems, communication systems, and control techniques for facilitating seamless, safe, and reliable interaction between the grid, fast EV chargers (FEVCs), and the EV. Furthermore, the review investigates the interaction between the grid and FEVCs through ancillary services, and highlights their potential to enhance grid stability and reliability. Although a significant research is conducted in the literature and a significant progress is achieved, a notable gap persists between the available academic literature and commercially deployed solutions. Investigating this gap is crucial for accelerating the adoption of FEVC technologies and maximizing their benefits for both consumers and grid operators. Therefore, a reference design is synthesized that combines the recommended elements identified throughout the literature.

Electric vehicle (EV) charging stations have been evolving to offer better and more efficient power delivery methods. A key area of research in this field involves predicting the power consumption of EV charging stations. Many researchers have addressed this issue using machine learning and deep learning methods, however, forecasting models struggle to predict individual charging sessions with high resolutions. In this paper, a deep neural network (DNN) is constructed to forecast the EV charging current profile and state of charge (SoC). Charging current and SoC are mathematically formalized and embedded into the DNN’s loss calculation to build a physics-informed neural network (PINN). The performance of the models is assessed through analysis of their prediction error, utilizing actual data from real EV charging sessions.

The Controller Area Network (CAN) Bus is a commonly used communication protocol for different applications including vehicle controllers and power electronics converters which are the application focus in this paper. One drawback of CAN-bus is the lack of security measures in the protocol, which can be problematic if the communication line is exposed to attackers. This paper proposes a novel method to secure CAN-bus communication that is light-weight and applicable for power converters and microgrid applications. The proposed solution aims to protect against eavesdropping, false data injection, and replay attacks. The security scheme is implemented and deployed on a low-cost microcontroller that is used to control power sharing rectifiers. Experimental results are carried out to show that the proposed method effectively secures the channel against attacks with minimal time and space overhead.