Electric vehicles (EVs) have a huge potential to dominate over the conventional combustion engine vehicles and become the future of road transport while conserving this planet from threatening calamities caused by global warming. EVs offer several benefits over internal combustion engines, especially in terms of lower emission of greenhouse gases, higher energy efficiency, and less dependency upon fossil fuels. Despite all of that, range anxiety, charging infrastructure are two significant limiting factors for the rapid adoption of EVs. This paper presents a thorough literature review on the latest deployment, and challenging issues in the implementation of EV infrastructure, charging power levels, in conjunction with various charging methods and examines the impacts of EVs on the electric grid.

The energy produced by renewable energy sources has variations as these sources are sensitive to weather conditions. To overcome this problem, storage devices like batteries are used for stabilization. Bidirectional DC-DC converter is the main component to interface renewable energy sources with energy storage devices. With the help of proper switching techniques, these converters have the ability to increase or decrease the level of the voltages and can handle the power flow in both directions, from source to storage device (forward direction/charging mode), as well as from storage device to source (backward direction/discharging mode). Hence for an efficient system, the control of power flow must be very effective. This paper presents an overview of various bidirectional DC-DC converter’s classical and supervisory control methods that have been proposed in recent years.

In this paper, implementation of a wearable glove is presented that can be used to assist the visually impaired people. It can help the blind person to avoid hurdles using haptic and voice feedback. It does not only navigate the path to the blind person but also informs about his/her location to others through a message on the mobile device. It can help his/her caretaker (in case he is not near to the blind person) to locate him using the received coordinates from the communication module of glove having Global Positioning System (GPS) and Global System for Mobile Communication (GSM) technology. This wearable glove is physically realized by interfacing different sensors and communication modules with Arduino UNO. Different modes of obstacles detection and alerts are provided using various sensors and actuators to assist visually impaired person.

E-mobility is an emerging means of transportation, mainly due to the environmental impact of petroleum-based fuel vehicles and oil prices’ peak. However, electric vehicles face several challenges by the nature of technology. Consequently, electric vehicles have a limited travel range and are extremely heavy. In this research, an investigation is carried out on different measures to reduce the DC-link capacitor size in the drive train of an electric vehicle. The investigation is based on software simulations. The DC-link capacitor must be dimensioned with regards to relevant points of operation, which are defined by the rotation speed and torque of the motor as well as the available DC-link voltage. This also includes the field-oriented control (FOC). In order to optimally operate a three-phase inverter in the electric drive train, a suitable type and sizing of the capacitor was studied based on mathematical equations and simulations. Two measures were examined in this study: firstly, an auxiliary passive notch filter introduced in the electric drive train circuit is explored. Based on this measure, an advanced modulation scheme exploiting the control of individual currents within segmented windings of the PMSM is investigated in detail. It was seen that saw-tooth carrier modulation used in the parallel three-phase inverter is found to reduce DC-link capacitor size in the electric drive train circuit by 70%.

Due to the high power density, inherent zero-voltage-switching (ZVS), and high voltage-conversation ratio, the current source-based isolated dual-active-bridge DAB–DC/DC converters are extensively used for charging EVs under constant-current mode. However, the fast dynamic response of an output current is a crucial requirement for dual-active-bridge DC/DC converters operating as a constant-current source. This study proposes a fast current controller (FCC) for tracking the desired output current under various input/output parameter disturbances/variations. The proposed control strategy can ensure a fast transient response with negligible overshoot/undershoot for output current during start-up and when there are variations in the load or input voltage. Furthermore, the dynamic behavior of the current control against change in the reference current value has also been improved. A constant-current-based DAB–DC/DC converter is modeled and simulated in MATLAB/Simulink software and a scaled-down 300 W lab prototype DAB–DC/DC converter is designed with the TMS320F28335 DSP controller of Texas Instruments. To verify the effectiveness of the proposed current controller, different test cases, such as a change in the load, a change in the input voltage, and a change in the desired output current, are considered. Moreover, under these test cases, the proposed current-control strategy is compared with the conventional proportional–integral (PI) current controller, model-based phase-shift controller (MBPS), and load current feed-forward controller (LCFF). Both the experimental and simulation results have validated the effectiveness of the proposed control strategy.

Phishing attacks remain a formidable threat in today's digital landscape, posing significant risks to individuals and organizations. The ever-evolving nature of these attacks outpaces conventional detection methods, demanding innovative solutions. This paper introduces a cutting-edge dual-layer model for phishing authentication, leveraging the combined power of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks on raw URL data. Our approach begins with the meticulous gathering and cleansing of diverse, up-to-date URL datasets to ensure a comprehensive foundation for analysis. The self-designed CNN extracts spatial patterns inherent in phishing URLs, while the LSTM network captures temporal dependencies and contextual nuances, significantly enhancing detection accuracy. This hybrid model achieves an impressive 98% accuracy, outstripping traditional machine-learning techniques in precision and recall. Extensive experimentation confirms the superiority of our model, which not only minimizes false positives and negatives but maintains computational efficiency, making it suitable for real-time deployment. The study underscores the critical need for continuous dataset updates and model retraining to keep pace with emerging threats, ensuring robust protection in an increasingly perilous cyberspace. This work represents a significant advance in phishing detection, offering a scalable, high-performance solution that meets the challenges of today's dynamic threat environment.