Electric vehicles (EVs) could be used to address the issues of environmental pollution and the depletion of non-renewable energy resources. EVs, which are energized by a battery storage system, are becoming attractive because they keep the environment clean. Furthermore, the cost of EVs is becoming cheaper. Thus, EVs will become a significant load on utility distribution system in the future. EV chargers play a significant role in the expansion of EVs. The input current of an EV charger with a high total harmonic distortion (THD) and a high ripple distortion of the output voltage can impact battery life and battery charging time. Furthermore, the high cost and large size of the chargers are considered other issues in EV development. This work presents the complete design process of a universal EV charger with a special focus on its control algorithms. In this regard, a novel control algorithm based on the integration of voltage-oriented control (VOC) and the sinusoidal pulse-width modulation (SPWM) technique is proposed to ensure effective Levels 1, 2, and 3 battery charging. A simulation of the universal EV charger was conducted and assessed in MATLAB–Simulink. Moreover, a laboratory prototype was constructed with a TMS320F28335 digital signal processor (DSP) programmed as the controller to validate its operation and performance. The findings show that the proposed charger is able to provide a controllable and constant charging voltage for a variety of EVs, with an input current of low total harmonic distortion (THD) and an almost unity power factor.

Optimizing Economic Load Dispatch with Renewable Energy Sources via Differential Evolution Immunized Ant Colony Optimization Technique

One critical issue in the tracking systems based on photovoltaic (PV) is how to harvest highest power of the photovoltaic array; particularly when the system is operating in partially shaded conditions (PSCs) or varying irradiances. This study proposes particle swarm optimization (PSO) hybridization and cuckoo search algorithm (CSA) methods for maximum power point tracking (MPPT). The effectiveness of the proposed algorithm is validated and examined under various irradiance patterns. A comparison study in performance has been conducted between the proposed hybrid CSA-PSO method with the conventional P&O and PSO techniques. Several tests have been performed based on numerical simulations utilizing the programming software MATLAB/Simulink. The results demonstrated that the suggested hybrid technique yields smaller tracking time, higher power and greater efficiency than those of other traditional algorithms.

The Energy Management System (EMS) is critical for electric vehicle (EV) in order to optimize energy consumption, improve efficiency, and enhance vehicle performance. The EMS provides the optimization of energy distribution among various vehicle components, reduces energy losses and maximizes the vehicle's efficacy. The EMS reduces battery stress to prevent excessive charging and discharging cycles; thereby, decreases the necessity for premature battery replacement which, in turn, contributes to the battery's life time. The goal of this research is to develop robust control technique to maximize the use of energy storage systems, renewable energy sources and the bidirectional power flow associated with EVs. The proposed robust control approach is based on combination of flatness theory with artificial neural network. The controller is responsible for maintaining the voltage DC bus stabilized and enhancing the quality of the power fed to the EV side. The performance of controlled EMS is verified via computer simulation within MATLAB/SIMULINK environment. As compared to classical proportional-integral (PI) control, the computer results show the proposed controller (FEMS-ANN) gives higher power quality of EV, lower overshot level in the DC voltage, faster response to abnormal conditions, and less steady state error.

An effective energy management system (EMS) was designed based on the Stateflow (SF) approach for a grid-connected nanogrid (NG) composed of a photovoltaic (PV) array with a battery bank and supercapacitor (SC) energy storage system (ESS). The PV energy system, battery bank and SC (ESS), dual active bridge DC/DC converters, DC/AC inverters, control algorithms, and controllers were developed to test the operation of the NG. The average and high-frequency power components are separated using frequency division of the ESS power utilizing a low-pass filter; the average power is absorbed by the battery bank, while the high-frequency power is absorbed by the SC. The aim of this paper is to design an EMS to manage the energy of a grid-connected NG system considering the availability of the PV array, ESS, and demand requirements. Different scenarios of operation were tested to check the EMS behaviour during the day with a random demand profile, including: (1) a PV array with the grid supplying the load without an EMS; (2) a PV array, batteries, and the grid supplying the load with an EMS; (3) a PV array, batteries, an SC, and the grid supplying the load with an EMS; (4) a PV array, batteries, an SC, and the grid supplying the load with an EMS, with load profile reduction by 20% due to COVID-19. As per the simulation results, the proposed EMS enables the flow of power in the NG system and demonstrates the impact on the ESS by minimising carbon emissions via a reduction in grid consumption. Furthermore, the SF method is regarded as a helpful alternative to popular design approaches employing conventional software tools.

The Healthcare Internet of Things (H-IoT) has made significant strides in transforming remote diagnostics, real-time monitoring, and data collection. However, achieving low latency remains a challenge, even for systems that critically depend on it, such as emergency response systems and remote surgeries. Fog computing (FC) emerges as a promising solution to mitigate latency issues by bringing computational resources closer to edge devices. This review provides a comprehensive analysis of latency optimization approaches within the H-IoT, framed within the context of FC. The study focuses on architectural enhancements, data communication methods, resource management strategies, and load distribution techniques aimed at reducing latency. Additionally, it evaluates existing challenges related to energy efficiency and trade-offs, security considerations, and scalability potential. Based on an analysis of recent advancements in the field, this study outlines potential development trends and future research directions for building adaptive, fast, and secure H-IoT systems. The study reaffirms the pivotal role of FC in shaping the future of healthcare services, particularly those with stringent low-latency requirements.