Mohd. Hasan Ali

With the ever-evolving and constantly growing need for electric vehicles (EVs) in the automotive industry, the importance of reliable, efficient, and dynamic EV chargers is increasing. Power electronics is the enabler and forms the execution layer of any charging station. EV charging must now integrate fast control, wide adaptability, bidirectionality, and reliability. The existing literature provides overview studies on the EV charging system, its infrastructure, and necessary power electronics. However, the integration of 5G-enabled EV charging (i.e., data transmission/communication via the 5G network) has introduced new performance expectations that go beyond the capabilities of conventional power converters. This paper presents an in-depth overview of power electronics applications in a 5G-enabled EV charging system. Several key aspects such as advanced converter topologies, a resonant converter for 5G charging, the integration of power electronics with 5G communication, and anomaly and intrusion detection models for a 5G-enabled Blink-2-level EV charger are discussed. Moreover, the challenges and risks of integrating 5G into EV charging infrastructure are discussed. Some recommendations on future research opportunities are provided. This study provides a basic guideline on power electronics applications in a 5G-enabled EV charging system and is therefore valuable to the researchers, scientists, and engineers working in this interesting field.

Offshore wind farms are increasingly and rapidly expanding due to their ability to harness strong and consistent wind energy resources. Large offshore wind farms are connected to mainland grids through High-Voltage Direct Current (HVDC) technology. However, offshore wind farms can often experience disturbances related to sudden wind changes, voltage drops/dips, faults related to converter switching, and unbalanced grid conditions which affect both the HVDC operation and wind turbine output. As a result, there is a growing need for more advanced and reliable modeling and monitoring tools. Moreover, traditional proportional-integral (PI) controllers are widely applied in wind turbines and HVDC systems due to their simple structure, easy implementation, and reliability. However, PI controllers perform poorly under non-linear and abnormal/fast-changing conditions, especially during sudden drops in wind power and grid faults. With this background, this paper first develops a digital twin model of an offshore wind farm that enables remote operation and monitoring of individual wind turbines. Also, an artificial intelligence (AI)-based controller, namely a fuzzy logic controller (FLC), is proposed to maintain transient stability of a full digital twin-based offshore wind farm connected to the HVDC grid under fault conditions. The effectiveness of the proposed FLC is demonstrated by considering a digital twin-enabled 700 MW offshore wind farm. The performance of the proposed FLC has been compared with that of the PI controller. Simulations performed by the MATLAB/Simulink software show that during the moderate voltage dip at 15 s, the PI controller experienced a 29.8% power reduction with a recovery time of approximately 9 s, whereas the FLC reduced the power drop to 23.1% and recovered within 6 s. During the severe converter disturbance at 15 s, the PI controller recorded a 36.9% power reduction compared to 23.4% for the FLC. Similarly, during the short-duration turbulence at 15 s, the PI controller exhibited a 36.73% power drop and recovered in approximately 7 s, while the FLC limited the power reduction to 19.17% and recovered within 5s. Overall, the FLC provided improved voltage stability, faster recovery, reduced oscillations, and superior fault ride-through capability compared with the conventional PI controller, demonstrating its effectiveness for digital twin-enabled offshore wind farm application.

The rapid increase in the adoption of electric vehicles (EVs) in recent years is leading to a significant impact on the electric grid. To ensure sufficient power to these EVs, multiple electric vehicle charging stations (EVCSs) need to be deployed strategically in the electrical power network. Randomly adding these EVCSs can cause potential power quality problems and necessitate additional infrastructure like new distribution/transmission lines, transformers and sub-stations. This can be overcome by optimal deployment of EVCSs. Many existing optimization techniques suffer from premature convergence, sensitivity to initial parameters, the curse of dimensionality and not performing well on non-linear problems. This leads to suboptimal results. To address these drawbacks, a novel method, based on the Artificial Hummingbird Algorithm (AHA), has been developed to identify the optimal location of EVCSs. The novel method, the Modified Artificial Hummingbird Algorithm (MAHA), has been applied to the standard power network–IEEE-57 bus system to find the optimal placement of EVCSs. When compared to existing methods of AHA, Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO), the results show that MAHA is more effective in determining the optimal placement of EVCSs.

Dynamic wireless charging (DWC) of electric vehicles (EVs) offers a promising solution to mitigate range anxiety and enhance the feasibility of electrified transportation; however, achieving optimal power transfer requires precise alignment between the primary coil embedded in the roadway and the secondary coil mounted on the vehicle. In practice, lateral misalignment (LTM) frequently occurs, leading to reduced efficiency. Although conventional controllers can partially compensate for these losses, their performance degrades under significant misalignment, resulting in overshoot and steady-state error (SSE). To overcome these limitations, this paper proposes a model predictive control (MPC)-based approach to mitigate the effects of LTM and restore efficient power transfer. A comparative study between the proposed MPC and a conventional proportional–integral (PI) controller is conducted to assess performance and suitability. The MPC utilizes an optimization framework to determine optimal control actions over a prediction horizon, thereby minimizing SSE and reducing overshoot under varying misalignment conditions. The effectiveness of the proposed method is validated through MATLAB/Simulink simulations and experimental testing. The results demonstrate that the MPC maintains stable operation over a wide LTM range, achieving a maximum power transfer efficiency of 93% at zero misalignment, which decreases to 83% at severe misalignment (LTM = 0.5). Compared to the PI controller, the MPC improves average efficiency by approximately 8–12%, leading to improved robustness and smoother dynamic response. These results confirm the effectiveness of the proposed MPC approach in maintaining high efficiency and stable operation in misaligned DWC systems.

Internet-of-things (IoT)-enabled smart grids modernize electricity infrastructure by integrating sensors, smart meters, and communication technologies to create a two-way, real-time, data-driven network. This enables automated monitoring, self-healing capabilities, efficient renewable energy integration, and improved load management, reducing outages and operational costs. However, there are significant gaps in current IoT-smart grid research, particularly in cybersecurity, interoperability, and scalability. Key challenges include securing, decentralized, and trustworthy IoT-driven monitoring; integrating variable renewable energy sources; establishing unified, standardized communication protocols; and ensuring data management and real-time data processing for large-scale, heterogeneous systems. Also, prior reviews did not clarify applications where IoT can be implemented in smart grids. To overcome the gaps and limitations in existing reviews, this paper presents an in-depth overview of the architecture, key technologies, possible areas of smart grids where IoT devices and technologies can be implemented, and cybersecurity issues of IoT. An extensive bibliography on the IoT applications in smart grids has been provided. Prioritized research gaps have been identified that focus on enhancing cybersecurity, improving interoperability among diverse devices, and managing massive data volumes for real-time stability. The challenges and future work for integration of IoT for smart grids are also discussed. This work benefits the readers and researchers and serves as a basis to understand the benefits of IoT in smart grids and the associated challenges to come over in implementation of these systems.

The increasing integration of electric vehicles (EVs) and renewable energy sources has accelerated the adoption of DC microgrids, where maintaining voltage stability and effective power sharing remains a critical challenge. Hybrid energy storage systems (HESS), combining batteries and supercapacitors, are commonly employed to address dynamic power variations. However, conventional proportional–integral (PI)-based control strategies for HESS can exhibit performance limitations under nonlinear and varying operating conditions. To overcome this drawback, this paper presents an adaptive neuro-fuzzy inference system (ANFIS)-based control strategy for HESS located in a DC microgrid, with comparative evaluation against both conventional PI and traditional Fuzzy Logic controller (FLC) schemes. The proposed approach is evaluated using a detailed MATLAB/Simulink R2024a model of a DC microgrid including EVs. Simulation results show that, under normal operating conditions, the ANFIS-based control demonstrates improved transient response, reduced voltage fluctuations, and effective coordination between the battery and supercapacitor during renewable power variations, compared to PI and FLC-controlled systems. In addition to nominal performance assessment, this work investigates the vulnerability of the ANFIS controller to cyber-attacks. Two representative attack scenarios, false data injection (FDI) and denial-of-service (DoS), are applied to critical measurement and control signals of HESS. Simulation results reveal that, although the DC-bus voltage regulation is largely maintained during attack intervals, cyber manipulation significantly disrupts the intended HESS power-sharing behavior.

Reliable and sustainable electricity access remains a critical challenge for rural educational institutions in developing countries such as Bangladesh, where frequent grid interruptions disrupt academic activities and essential services. This study proposes a hydrogen‐enabled hybrid renewable microgrid designed to provide dependable and environmentally sustainable electricity for a remote secondary school. The primary objective is to develop a cost‐effective, low‐carbon and resilient energy system that minimizes reliance on fossil‐fuel‐based grid electricity while ensuring an uninterrupted power supply. The proposed microgrid integrates solar photovoltaic generation, a hydrogen production system with an electrolyser, hydrogen storage, a fuel cell, battery energy storage and limited grid support. The system configuration is optimized using HOMER Pro to determine the most economical and efficient design. In addition, MATLAB‐based simulations are conducted to evaluate the voltage and frequency stability of the microgrid under different operating conditions. A deep learning model is also implemented to forecast battery state‐of‐charge, enabling improved energy management and operational reliability. The optimized system achieves a cost of energy (COE) of $0.0395/kWh and a net present cost of $18,078. Moreover, annual CO ₂ emissions are reduced by 84.7% (5447 kg) compared with conventional fossil‐based electricity generation systems.

The increasing adoption of electric vehicles (EVs) and the integration of 5G/6G networks are driving the demand for secure, intelligent, and interoperable charging infrastructure within the Internet of Vehicles (IoV) ecosystem. Electric Vehicle Charging Stations (EVCS) face growing cyber–physical threats, including spoofing, data injection, and firmware tampering, risking user privacy, grid stability, and EVCS reliability. While artificial intelligence (AI), blockchain, and cryptography have been applied in cybersecurity, comprehensive solutions tailored to EVCS challenges, such as real-time threat mitigation and scalability, are often lacking. This paper addresses these critical cybersecurity gaps by presenting a comprehensive overview of novel strategies for enhancing EVCS security through the Internet of Digital Twins (IoDT) technology. The primary objective is to evaluate advanced frameworks that synergize digital twins with artificial intelligence, blockchain, and quantum-resistant cryptography. Through systematic literature analysis, global threat assessments, and review of international standards, this study identifies key attack vectors and their impacts on EVCS. Key findings demonstrate that digital twin-driven solutions facilitate real-time monitoring, anomaly detection, predictive threat mitigation, and secure system governance. This review offers actionable insights for researchers, industry stakeholders, and policymakers to strengthen the cybersecurity and resilience of next-generation electric mobility infrastructure, addressing challenges like scalability and implementation barriers.

Smart buildings require an energy management system that can meet inhabitants’ demands with a reduced amount of energy consumed by the heating ventilation and air-conditioning system (HVAC), as well as the lighting and shading systems. This work provides a detailed review of available methods proposed in the literature for effective control of automated systems such as HVAC, lighting, shading, etc. Moreover, effective forecasting of renewable energy generations and loads, scheduling of loads, and efficient operations of thermal and electric energy storage are crucial elements for energy management systems for ensuring reliability and stability. In this work, these aspects of energy management systems, that have been popular over the last ten years, are analyzed. In addition, the development of internet-of-things (IoT)-based sensors widens the artificial intelligence (AI) and machine learning applications in smart buildings. However, this system can be vulnerable against cyber-attacks. The state of the art of AI and machine learning applications along with cyber security issues and solutions for smart building systems are discussed. Finally, some recommendations for future research trends and directions on smart building systems are provided. This work will provide a basic guideline and will also be very useful to researchers in the area of smart building systems in the future.