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The global reliance on electric vehicles (EVs) has been rapidly increasing due to the excessive use of fossil fuels and the resultant CO2 emissions. Moreover, EVs facilitate using alternative energy sources, such as energy storage systems (ESSs) and renewable energy sources (RESs), promoting mobility while reducing dependence on fossil fuels. However, this trend is accompanied by multiple challenges related to EVs' traction systems, storage capacity, chemistry, charging infrastructure, and techniques. Additionally, the requisite energy management technologies and the standards and regulations needed to facilitate the expansion of the EV market present further complexities. This paper provides a comprehensive and up-to-date review of the state of the art concerning EV-related components, including energy storage systems, electric motors, charging topologies, and control techniques. Furthermore, the paper explores each sector's commonly used standards and codes. Through this extensive review, the paper aims to advance knowledge in the field and support the ongoing development and implementation of EV technologies. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are the new paradigm of decentralized networks of renewable energy sources, loads, and storage devices that can operate independently or in coordination with the primary grid, incorporating significant flexibility and supply reliability. To increase reliability, traditional individual MGs can be replaced by networked microgrids (NMGs), which are more dependable. However, when it comes to operation and control, they also pose challenges for cyber security and communication reliability. Denial of service (DoS) is a common danger to DC microgrids with advanced controllers that rely on active information exchanges and has been recorded as the most frequent cause of cyber incidents. It can disrupt data transmission, leading to ineffective control and system instability. This paper proposes digital twin (DT) technology as an integrated solution, with new, advanced analytics technology using machine learning and artificial intelligence to provide simulation capabilities to predict and estimate future states. By twinning the cyber-physical dynamics of NMGs using data-driven models, DoS attacks targeting cyber-layer agents will be detected and mitigated. A long short-term memory (LSTM) model data-driven digital twin approach for DoS attack detection and mitigation is implemented, tested, and evaluated. [ABSTRACT FROM AUTHOR]

Batteries are considered critical elements in most applications nowadays due to their power and energy density features. However, uncontrolled charging and discharging will negatively affect their functions and might result in a catastrophic failure of their applications. Hence, a battery management system (BMS) is mandated for their proper operation. One of the critical elements of any BMS is the state of charge (SoC) estimation process, which highly determines the needed action to maintain the battery's health and efficiency. Several methods were used to estimate the Lithium‐ion batteries (LIBs) SoC, depending on the LIBs model or any other suitable technique. This article provides a critical review of the existing SoC estimation approaches and the main LIB models with their pros and cons, their possibility to integrate with each other's for precise estimation results, and the applicability of these techniques in electric vehicles and utility applications with the commonly used standards and codes in these sectors. Moreover, this study will also explore a future framework for integrating digital twins (DTs) with BMSs for improved and advanced management. Based on this comprehensive review, it can be concluded that merging the model‐based estimation techniques with the data‐driven approaches with their promising development to determine the dynamic patterns inside the battery can efficiently achieve precise estimation results while reducing the complexity of these models. This integration will align also with the integration of digital twin technology to provide complete and accurate supervision for the BMSs. This article is categorized under:Emerging Technologies > Energy StorageEmerging Technologies > DigitalizationEnergy and Power Systems > Distributed Generation [ABSTRACT FROM AUTHOR]