The adoption of electric vehicles (EVs) has seen exponential growth globally, driving the urgent need for efficient and widespread fast-charging infrastructure. This paper reviews the current state of EV fast charging technologies, including DC fast charging (Level 3), ultra-fast chargers, and emerging megawatt charging systems. It also analyzes key technical challenges such as grid integration, battery thermal management, charger standardization, and cost factors. The study explores innovations like solid-state battery compatibility, V2G (Vehicle-to-Grid) technology, and AI-driven charge optimization. The paper concludes with recommendations for future research and policy strategies to support scalable, sustainable, and user-centric EV charging ecosystems.

As modern power grids become increasingly complex, the need for faster and more intelligent fault detection systems is growing. Conventional approaches often suffer from inefficiency, delayed response, and limited predictive capability. This paper investigates the role of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), in enhancing smart grid reliability. By utilizing real-time data from sensors, phasor measurement units (PMUs), and intelligent electronic devices (IEDs), AI-based systems detect and classify faults with higher speed and accuracy. ML models such as Support Vector Machines, Random Forests, and Artificial Neural Networks enable anomaly detection and predictive fault diagnosis. Meanwhile, Deep Learning methods including Convolutional and Recurrent Neural Networks strengthen pattern recognition for improved fault classification. Compared with traditional techniques, AI-driven systems provide predictive maintenance, rapid fault recovery, and real-time responsiveness. Integration with cloud and edge computing further supports scalability. Although challenges remain—such as data privacy, computational cost, and the need for quality datasets—emerging solutions like federated learning and hybrid AI models promise more resilient, self-healing power grids.

This project explores the integration of solar energy with electric vehicle (EV) technology to promote sustainable and eco-friendly transportation. The primary objective is to reduce reliance on fossil fuels by employing solar panels to generate electricity for EV operation. Photovoltaic (PV) cells, primarily silicon-based, are mounted on the vehicle’s roof, hood, and other flat surfaces, providing an effective solar area of about 4.5–6 m². The harvested energy is stored in solid-state batteries, which offer higher energy density, enhanced safety, and improved efficiency compared to conventional batteries. The system supports real-time solar charging, decreases dependency on the electrical grid, and extends vehicle range, thereby enhancing overall performance. By combining solar technology with advanced battery storage and management systems, the proposed design contributes to sustainable mobility solutions and a greener future.

In this paper a compact metamaterial incorporated microstrip patch antenna is presented. A hybrid structure, which is a combination of Boolean addition and subtraction of self-similar hexagonal geometries at different scale which is designed at the top layer of the FR-4 substrate (tan (?) = 0.02, ?r= 4.3, h= 1.6). A modified ground plane is also designed at bottom layer of substrate to meet resonance criteria. Also, a hybrid metamaterial parasitic element is incorporated on the ground plane for performance improvement. On energizing with the microstrip feed line the simulated antenna exhibits five resonance frequencies 1.11, 1.7, 2.18, 2.8, and 3.5 GHz. It has covered four frequency band-1(1.01- 1.25 GHz), band-2 (1.52-1.93 GHz) band-3 (2.15- 2.24 GHz) and band-4 (2.25- 3.95 GHz) for |S11| ? ?10 dB which are suitable for several wireless communication applications.