This paper presents a detailed literature review on switched reluctance motor (SRM) and drive systems in electric vehicle (EV) powertrains. SRMs have received increasing attention for EV applications owing to their reliable structure, fault tolerance ability and magnet free design. The main drawbacks of the SRM are torque ripple, low power density, low power factor and small extended speed range. Recent research shows that multi-stack conventional switched reluctance motors (MSCSRM) and multi-stack switched reluctance motors with a segmental rotor (MSSRM-SR) are promising alternative solutions to reduce torque ripples, increase torque density and increase power factor. Different winding configurations such as single-layer concentrated winding (SLC), single layer mutually coupled winding (SLMC), double layer concentrated winding (DLC), double layer mutually coupled winding (DLMC) and fully-pitched winding (FP) are introduced in the literature in recent years to increase average torque and to decrease torque ripples. This research analyzes winding methods and structure of the SRMs, including conventional and segmental rotors. They have been compared and assessed in detail evaluation of torque ripple reduction, torque/power density increase, noise/vibration characteristics and mechanical structure. In addition, various drive systems are fully addressed for the SRMs, including conventional drives, soft-switching drives, drives with standard inverters and drives with an integrated battery charger. In this paper, the SRM control methods are also reviewed and classified. These control methods include strategies of torque ripple reduction, fault-diagnosis, fault-tolerance techniques and sensorless control. The key contributions of this paper provide a useful basis for detailed analysis of modeling and electromechanical design, drive systems, and control techniques of the SRMs for EV applications.

A modular multilevel converter (MMC) is an advanced voltage source converter applicable to a wide range of medium and high-voltage applications. It has competitive advantages such as quality output performance, high modularity, simple scalability, and low voltage and current rating demand for the power switches. Remarkable studies have been carried out regarding its topology, control, and operation. The main purpose of this review is to present the current state of the art of the MMC technology and to offer a better understanding of its operation and control for stationary applications. In this study, the MMC configuration is presented regarding its conventional and advanced submodule (SM) and overall topologies. The mathematical modeling, output voltage, and current control under different grid conditions, submodule balancing control, circulating current control, and modulation methods are discussed to provide the state of the MMC technology. The challenges linked to the MMC are associated with submodule balancing control, circulating current control, control complexity, and transient performance. Advanced nonlinear and predictable control strategies are expected to improve the MMC control and performance in comparison with conventional control methods. Finally, the power losses associated with the advanced wide bandgap (WBG) power devices (such as SiC, GaN) are explored by using different modulation schemes and switching frequencies. The results indicate that although the phase-shifted carrier-based pulse width modulation (PSC-PWM) has higher power losses, it outputs a better quality voltage with lower total harmonic distortion (THD) in comparison with phase-disposition pulse width modulation (PD-PWM) and sampled average modulation pulse width modulation (SAM-PWM). In addition, WBG switches such as silicon carbide (SiC) and gallium nitride (GaN) devices have lower power losses and higher efficiency, especially at high switching frequency in the MMC applications.

Nowadays, Wireless Power Transfer (WPT) technology is receiving more attention in the automotive sector, introducing a safe, flexible and promising alternative to the standard battery chargers. Considering these advantages, charging electric vehicle (EV) batteries using the WPT method can be an important alternative to plug-in charging systems. This paper focuses on the Inductive Power Transfer (IPT) method, which is based on the magnetic coupling of coils exchanging power from a stationary primary unit to a secondary system onboard the EV. A comprehensive review has been performed on the history of the evolution, working principles and phenomena, design considerations, control methods and health issues of IPT systems, especially those based on EV charging. In particular, the coil design, operating frequency selection, efficiency values and the preferred compensation topologies in the literature have been discussed. The published guidelines and reports that have studied the effects of WPT systems on human health are also given. In addition, suggested methods in the literature for protection from exposure are discussed. The control section gives the common charging control techniques and focuses on the constant current-constant voltage (CC-CV) approach, which is usually used for EV battery chargers.

The significant role of Li-ion batteries (LIBs) in electric vehicles (EVs) emphasizes their advantages in terms of energy density, being lightweight, and being environmentally sustainable. Despite their obstacles, such as costs, safety concerns, and recycling challenges, LIBs are crucial in terms of the popularity of EVs. The accurate prediction and management of LIBs in EVs are essential, and machine learning-based methods have been explored in order to estimate parameters such as the state of charge (SoC), the state of health (SoH), and the state of power (SoP). Various machine learning techniques, including support vector machines, decision trees, and deep learning, have been employed for predicting LIB states. This study proposes a methodology for comparative analysis, focusing on classical and deep learning approaches, and discusses enhancements to the LSTM (long short-term memory) and Bi-LSTM (bidirectional long short-term memory) methods. Evaluation metrics such as MSE, MAE, RMSE, and R-squared are applied to assess the proposed methods’ performances. The study aims to contribute to technological advancements in the electric vehicle industry by predicting the performance of LIBs. The structure of the rest of the study is outlined, covering materials and methods, LIB data preparation, analysis, the proposal of machine learning models, evaluations, and concluding remarks, with recommendations for future studies.

Fossil fuels still have emerged as the predominant energy source for power generation on a global scale. In recent years, Turkey has experienced a notable decrease in the production of coal and natural gas energy, juxtaposed with a significant rise in the production of renewable energy sources. The study employed neural networks, ANNs (artificial neural networks), and LSTM (long short-term memory), as well as CNN (convolutional neural network) and hybrid CNN-LSTM designs, to assess Turkey’s energy potential. Real-time outcomes were produced by integrating these models with meteorological data. The objective was to design strategies for enhancing performance by comparing various models of outcomes. The data collected for Turkey as a whole are based on average values. Machine learning approaches were employed to mitigate the error rate seen in the acquired outcomes. Comparisons were conducted across light gradient boosting machine (LightGBM), gradient boosting regressor (GBR), and random forest regressor (RF) techniques, which represent machine learning models, alongside deep learning models. Based on the findings of the comparative analyses, it was determined that the machine learning model, LightGBM, exhibited the most favorable performance in enhancing the accuracy of predictions. Conversely, the hybrid model, CNN-LSTM, had the greatest rate of inaccuracy. This study will serve as a guide for renewable energy researchers, especially in developing countries such as Turkey that have not switched to a smart grid system.

Managing the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale applications, enhances the cost-effectiveness of energy storage systems. Less frequent replacement or maintenance of LiBs results in cost savings in the long term. Therefore, in this study, AdaBoost, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble learning models were employed to predict the discharge capacity of LiBs. The prediction performances of each model were compared based on mean absolute error (MAE), mean squared error (MSE), and R-squared values. The research findings reveal that the LightGBM model exhibited the lowest MAE (0.103) and MSE (0.019) values and the highest R-squared (0.887) value, thus demonstrating the strongest correlation in predictions. Gradient boosting and XGBoost models showed similar performance levels but ranked just below LightGBM. The competitive performance of the ensemble model indicates that combining multiple models could lead to an overall performance improvement. Furthermore, the study incorporates an analysis of key features affecting model predictions using SHAP (Shapley additive explanations) values within the framework of explainable artificial intelligence (XAI). This analysis evaluates the impact of features such as temperature, cycle index, voltage, and current on predictions, revealing a significant effect of temperature on discharge capacity. The results of this study emphasize the potential of machine learning models in LiB management within the XAI framework and demonstrate how these technologies could play a strategic role in optimizing energy storage systems.