According to the current directive, we must rely on green energy for sustainable mobility. One of the green transition’s goals is to use renewable energy to charge electric vehicles (EVs). Solar energy is a form of renewable energy sources, and it is classified as being clean, available, and renewable because it is based on sunshine. Nowadays, the world is turning to EVs which have lower running costs and cleaner environments. Lithium-ion batteries are commonly utilized to store energy in EVs. This article covers the design and analysis of a photovoltaic (PV) system to charge five models of EVs such as BMW i3 2019, Volkswagen e-Golf, Fiat 500e, Mercedes EQA 250, and Hyundai Kona Electric in a DC fast charging mode by using a buck converter to minimize the output voltage and without any addition of energy storage. By applying Perturb & Observe (P&O) Maximum Power Point Tracking (MPPT), the maximum power and efficiency from PVs are obtained. The charging time is calculated for each EV of the five models in the State of Charge (SOC) area at 20–80 percent. A MATLAB program is employed to simulate the EV models by calculating the efficiency of the MPPT controller, time of charging, and characteristic of voltage and current levels for each model of these EVs. All models are tested under the condition of irradiance level from 600 W/m2 to 1000 W/m2 and temperature between 20°C and 30°C. The results showed that the PV system is effective and economical as a stand-alone to charge EVs in a rapid charge mode.

Low-frequency oscillations are an inevitable phenomenon of a power system. This paper proposes an Ant lion optimization approach to optimize the dual-input power system stabilizer (PSS2B) parameters to enhance the transfer capability of the 400 kV line in the North-West region of the Ethiopian electric network by the damping of low-frequency oscillation. Double-input Power system stabilizers (PSSs) are currently used in power systems to damp out low-frequency oscillations. The gained minimum damping ratio and eigenvalue results of the proposed Ant lion algorithm (ALO) approach are compared with the existing conventional system to get better efficiency at various loading conditions. Additionally, the proposed Ant lion optimization approach requires minimal time to estimate the key parameters of the power oscillation damper (POD). Consequently, the average time taken to optimally size the parameters of the PSS controller was 14.6 s, which is pretty small and indicates real-time implementation of an ALO developed model. The nonlinear equations that represent the system have been linearized and then placed in state-space form in order to study and analyze the dynamic performance of the system by damping out low-frequency oscillation problems. Finally, conventional fixed-gain PSS improves the maximum overshoot by 5.2% and settling time by 51.4%, but the proposed optimally sized PSS employed with the ALO method had improved the maximum overshoot by 16.86% and settling time by 78.7%.

Central-type photovoltaic (PV) inverters are used in most large-scale standalone and grid-tied PV applications due to the inverter’s high efficiency and low-cost per kW generated. The perturbation and observation (P&O) and incremental conductance (IncCond) have become the most common techniques for maximum power point tracking (MPPT) strategies of PV/wind generation systems. Typically, the MPPT technique is applied in a two-stage operation; the first stage tracks the MPP and boosts the PV voltage to a certain level that complies with grid voltage, whereas the second stage represents the inversion stage that ties the PV system to the grid. Therefore, these common configurations increase the system size and cost as well as reduce its overall footprint. As a result, this paper applies two IncCond MPPT techniques on a proposed single-stage three-phase differential-flyback inverter (DFI). In addition, the three-phase DFI is analyzed for grid current negative-sequence harmonic compensation (NSHC). The proposed system efficiently provides a MPPT of the PV system and voltage boosting property of the DC-AC inverter in a single-stage operation. Moreover, the MPPT technique has been applied through the DFI using the conventional and modified IncCond tracking strategies. Furthermore, the system is validated for the grid-tied operation with the negative-sequence harmonic compensation strategy using computer-based simulation and is tested under uniform, step-change, as well as fast-changing irradiance profiles. The average efficiencies of the proposed system, considering the conventional and modified IncCond MPPT techniques, are 94.16% and 96.4% with tracking responses of 0.062 and 0.035 s and maximum overshoot of 46.15% and 15.38%, respectively.

In recent years, coreless axial-flux permanent-magnet (AFPM) machines have been gaining attention. Being coreless, these machines do not experience eddy current (hence hysteresis losses) and has lower cogging torque leading to higher efficiency. However, limitations such as high leakage flux and weak mechanical structure impede its wide application. The results of a systematic review on coreless AFPM machine research is presented, wherein a total of 123 studies have been selected through the Preferred Reported Items for Systematic Reviews and Meta-Analysis (PRISMA) protocol. About 55% of the articles have been published between 2017 and 2021, indicating recent attention of the researchers on coreless AFPM machines. More than 70% (87 out of 123) of the records have been published by IEEE, which proves the quality and acceptance of the studies. In two-thirds (77 out of 123) of the studies, single-stator double-rotor has been adopted as the machine topology, and in 71.8% (84 out of 123) cases, trapezoidal shape magnets have been used in conventional array. The key research areas on coreless AFPM identified through this review are to produce an efficient design for multiphase and multistage topology and develop lightweight rotor and stator structures.

Electric vehicle (EV) markets have evolved. In this regard, rechargeable batteries such as lithium-ion (Li-ion) batteries become critical in EV applications. However, the nonlinear features of Li-ion batteries make their performance over their lifetime, reliability, and control more difficult. In this regard, the battery management system (BMS) is crucial for monitoring, handling, and improving the lifespan and reliability of this type of battery from cell to pack levels, particularly in EV applications. Accordingly, the BMS should control and monitor the voltage, current, and temperature of the battery system during the lifespan of the battery. In this article, the BMS definition, state of health (SoH) and state of charge (SoC) methods, and battery fault detection methods were investigated as crucial aspects of the control strategy of Li-ion batteries for assessing and improving the reliability of the system. Moreover, for a clear understanding of the voltage behavior of the battery, the open-circuit voltage (OCV) at three ambient temperatures, 10 °C, 25 °C, and 45 °C, and three different SoC levels, 80%, 50%, and 20%, were investigated. The results obtained showed that altering the ambient temperature impacts the OCV variations of the battery. For instance, by increasing the temperature, the voltage fluctuation at 45 °C at low SoC of 50% and 20% was more significant than in the other conditions. In contrast, the rate of the OCV at different SoC in low and high temperatures was more stable.