Currently, the electrification of transport networks is one of the initiatives being performed to reduce greenhouse gas emissions. Despite the rapid advancement of power electronic systems for electrified transportation systems, their integration into the AC power grid generates a variety of quality issues in the electrical distribution system. Among the possible solutions to this challenge is the inclusion of continuous storage systems, which can be located either onboard or offboard. The rapid development of energy storage devices has enabled the creation of numerous solutions that are leading to ever-increasing energy consumption efficiency, particularly when two or more of these storage systems are linked in a cascade and a hybrid mode. The various energy storage systems that can be integrated into vehicle charging systems (cars, buses, and trains) are investigated in this study, as are their electrical models and the various hybrid storage systems that are available.

Activity monitoring has become a necessary demand for weak people to guarantee their safety. The paper proposed a Parallel Training Logical Execution (PTLE) system using machine learning (ML) models on a microelectromechanical system (MEMS) accelerometer to detect coughs, falls, and other normal activities. When there are many categories, the ML prediction can be confused between these activities with each other. The PTLE system trains several models in parallel with more specific activity classes in each dataset. The shared tasks between parallel models relieve the complexity for a single one. There are six additional parameters for accelerometer characteristics, which were calculated from three axes accelerations as input features to improve the ML’s consciousness. Once all models were trained, the system was ready to receive the input accelerations and activated the logical flow to manage link operation between these ML models for output predictions. Random Forest (RF) had the highest potential among the ML classification algorithms after the validation. In the experiment, the comparison between the PTLE model and the regular ML model were carried out with real-time data from an M5stickC wearable device on the user’s chest to the trained models on PC. The result showed the advancement of the proposed method in term of precision, recall, F1-score with an overall accuracy of 98% in the real-time test. The accelerations from the wearable device were sent to ML models via Wi-Fi with Message Queue Telemetry Transport (MQTT) broker, and the activity predictions were transferred to the cloud for the family members or doctor care based on Internet of Things (IoT) communication.

The penetration of electric vehicles is becoming more and more widespread and recently electric buses and trains are fetching the attention of researchers and developers. In this review, we examine the charging systems applied to the fast charging of electric vehicles and buses as well as the charging strategies, mainly applied to electric buses. We also briefly delve into power topologies for a more electrified railway system where we discuss their different supply systems as well as their motor drive systems. Problems and charge challenges for electric buses and trains are discussed too.

This paper proposes a novel feedforward control scheme to achieve a very smooth transition from Constant Current (CC) to Constant Voltage (CV) charging modes, the commonly used method for electric vehicle charging applications. Furthermore, a three-loop model-independent Linear Active Disturbance Rejection Control (LADRC)-based system is proposed, replacing the traditional two-loop Proportional-Integral (PI) control system. The extra loop performs a decoupled dq vector control of the inductor current, which is typically not used in single-phase Dual Active Bridge (DAB) systems. This additional loop not only facilitates the optimal determination of both internal and external phase shift angles of a Dual-Phase Shift (DPS) modulator but also lowers the peak input current of the converter, allowing for lower-rated switches. Numerical simulations using MATLAB/Simulink demonstrate the robustness of the proposed control strategy against both input voltage disturbances and load disturbances during the transition from CC to CV charging modes. Hence, the dynamic performance of the charging system is significantly improved with minimal controller effort.

Electric vehicles (EVs) are rapidly replacing fossil-fuel-powered vehicles, creating a need for a fast-charging infrastructure that is crucial for their widespread adoption. This research addresses this challenge by improving the control of dual active bridge converters, a popular choice for high-power EV charging stations. A critical issue in EV battery charging is the smooth transition between charging stages (constant current and constant voltage) which can disrupt converter performance. This work proposes a novel feedforward control method using a combination of droop-based techniques combined with a sophisticated linear active disturbance rejection control system applied to a single-phase shift-modulated dual active bridge. This combination ensures a seamless transition between charging stages and enhances the robustness of the system against fluctuations in both input voltage and load. Numerical simulations using MATLAB/Simulink R2024a demonstrated that this approach not only enables smooth charging but also reduces the peak input converter current, allowing for the use of lower-rated components in the converter design. This translates to potentially lower costs for building these essential charging stations and faster adoption of EVs.

Distributed energy resources (DERs), such as photovoltaic (PV) sources, together with storage systems, such as battery energy storage systems (BESS), are increasingly present and necessary in our electricity distribution networks. Furthermore, the need for efficient use of energy from DERs, especially in developing countries and remote communities, must be addressed with the development of nanogrids (NGs), particularly DC NGs, and standalone PV systems with adequate control strategies. This paper investigates the control and dynamic operation of a standalone PV system. It consists mainly of three DC–DC power converters for the PV source interface, battery management, and load voltage control. A two-level modulation scheme is applied to each of these converters to switch them ON and OFF. A maximum power point tracking (MPPT) closed-loop voltage control system is implemented to make sure that the PV operates at optimum power regardless of the irradiance level or temperature, while battery voltage and load-side voltage control are also implemented to indirectly provide the required load power. The control of each of the converters is achieved by deriving their small-signal models using a state-space approach from which various control objectives are implemented. The DC-link is clamped by a BESS which acts as a backup source to provide power to the DC load in the absence of sufficient power from the PV panel. The dynamic operation of the whole system is enhanced by proposing a robust feedforward scheme that improves the response of the system in the presence of disturbances. The models are analyzed and implemented using PLECS, and numerical simulations are performed to validate the developed models and control schemes.