Electric vehicles (EVs) offer a promising solution for mitigating greenhouse gas emissions and minimizing the transportation sector's dependency on non-renewable energy sources. However, efficient energy management poses a significant challenge for their broader adoption, particularly optimizing battery usage, maximizing driving range, and improving overall vehicle performance. This paper presents the state-of-the-art Artificial Intelligence (AI) techniques used in electric vehicle energy management systems (EV-EMS), discussing a variety of deep learning algorithms of AI methodologies, such as, neural networks, and fuzzy logic. Additionally, This paper discusses the role of auxiliary techniques like transfer learning, which enhances model adaptability and reduces training time in AI-driven EMS applications. Through a systematic analysis of each method, this review identifies key trends, highlights the challenges and limitations of each technique, and offers perspectives on potential solutions and future research directions. The paper aims to support researchers, industry professionals, and policymakers in developing advanced, sustainable, and adaptable EV-EMS solutions that maximize battery life, improve vehicle performance, and facilitate real-time adaptive control. Finally, this review highlights the importance of AI-driven strategies in making EV technology more efficient, reliable, and scalable.

This paper introduces a novel single-loop control scheme for voltage regulation in islanded inverters, using a proportional-integral-lead (PI-Lead) controller designed within the synchronous reference frame (SRF) through a loop-shaping approach. Commonly, dual-loop controllers have been employed for this purpose owing to several limitations, such as insufficient stability with a narrow gain margin, a trade-off between stability and bandwidth, and constrained bandwidth due to the need for a significantly lower outer voltage loop bandwidth compared to the inner current one. The proposed method overcomes these challenges by integrating a Lead compensator, which enhances voltage regulation by eliminating steady-state error, improving stability margins, and providing a fast transient response while maintaining robustness against model parameter variations. Additionally, the control strategy reduces dependence on current measurements, except when dealing with inductive loads where virtual resistor-based active damping is necessary. Despite the challenges posed by multi-resonance phenomena and coupling effects inherent in single-loop SRF-based modeling, a comprehensive frequency-domain analysis is performed, with systematic controller parameter design guidelines to mitigate multi-gain crossover issues. Rigorous experimental results validate the theoretical findings and simulations, demonstrating the superior performance and practical effectiveness of the proposed control strategy compared to existing methods.

In this paper, a Reinforcement Learning controller (RLC) is designed and implemented on a 5-level Packed U-Cell (PUC5) grid-connected inverter to control the injected current flowing into the electric network.The RL agent is trained using a Proportional-Integral (PI) reward function to optimize its control strategy. Moreover, the voltage balancing of the auxiliary capacitor in PUC5 is separated from the RL controller and integrated into the switching algorithm to reduce the training burden. This modification reduces the observation inputs required for RL training, significantly shorten the training time. Simulation studies conducted in Matlab/Simulink evaluate the performance of the proposed RL controller, demonstrating robust dynamic response and accurate tracking of reference signals across different operational conditions.

This paper presents an innovative control strategy to enhance the stability of interconnected Microgrids (MGs) with low inertia and high penetration levels of Renewable Energies (REs). The proposed control strategy encompasses a new virtual droop control mechanism that emulates the primary control of synchronous generators for enhanced system stability. Additionally, a weighted Proportional-Integral (PI) controller is used to mitigate the adverse effects of measurement delays caused by Phase-Locked Loop (PLL) dynamics. Furthermore, a feedback integral loop is introduced to improve the efficiency and lifespan of Vanadium Redox Flow Batteries (VRFBs) enabling swift and precise power delivery while reducing steady-state errors. Finally, a new fractional-order virtual inertia control (VIC) is introduced to leverage the fractional derivatives and enhance the system's frequency response. The presented simulation results demonstrate the effectiveness of the proposed control approach in improving the frequency response and power exchange dynamics across interconnected MGs under various operating scenarios.

This study proposes the application of a Lyapunov-based Model Predictive Control (L-MPC) approach to a 9-level Crossover Switches Cell (CSC9) converter operating in grid connection mode. The proposed method utilizes the structure of the classical finite-control-set MPC (FCS-MPC) technique while integrating a cost function that requires no tuning. By deriving the cost function based on Lyapunov theory, the system stability is ensured. Notably, the suggested approach offers several advantages over traditional MPC controllers. Firstly, it eliminates the need for gain tuning, thereby simplifying the implementation process. Secondly, the proposed controller prioritizes stability as a key design aspect. The presented simulation results prove that the proposed controller effectively regulates the voltage of the DC capacitor around its desired value and feed a smooth sinusoidal current to the grid with low total harmonic distortion (THD) while operating at a unity power factor.

Multilevel converters (MLCs) are widely recognized for their exceptional benefits and have emerged as the preferred choice for medium- and high-power/voltage applications. Their usage has also been extended to low-power applications to overcome issues associated with high switching frequencies and electromagnetic interference (EMI) commonly encountered in two-level converters. Common dc-link MLCs have received particular attention in industry due to their ability to eliminate the need for bulky and inefficient transformers and rectifiers, making them a compelling option for different applications, primarily medium- and high-power/voltage applications such as wind turbine (WT) power conversion systems. Furthermore, common dc-link topologies are required for back-to-back (BTB) configurations, as they facilitate the use of a shared dc-link between the rectification and inversion stages. Despite their popularity, there is currently no comprehensive review article dedicated to common dc-link topologies. This article addresses this gap by presenting a comprehensive review of common dc-link MLCs, covering their topological evolution, features, topologies comparison, modulation techniques, control strategies, and industrial application areas. Additionally, future perspectives and recommendations are discussed to provide researchers and engineers with a better understanding of the potential applications and advantages of these converters.

Nowadays, single-dc-source multilevel inverter (SDCS-MLI) topologies are being considered as more suitable for many power system applications such as renewable energy conversion systems and electrified transportations compared to the multiple-dc-source MLIs. Voltage balancing of the auxiliary capacitors in those configurations is a major matter of concern. Different techniques have been developed to overcome this issue that can be mainly categorized as internal controller-based and external controller-based techniques. In the former techniques, the redundant switching states help balancing the capacitors voltages. On the other hand, the latter techniques involve external regulators to balance the capacitors' voltages. This article analyzes most of the existing techniques to control and balance the capacitors voltages in SDCS-MLIs, such as pulsewidth modulation, space vector modulation (SVM), hysteresis control, model predictive control, sliding mode, and artificial intelligence based control techniques. Furthermore, a comprehensive comparison is presented to illustrate the advantages/disadvantages of each technique. Finally, some industrial challenges and future works are projected.

In this paper, an optimized auto-tuning of Finite Control Set-Model Predictive Control (FCS-MPC) is proposed for a 9-level Crossover Switches Cell (CSC9) inverter. The system under study, a grid-connected single-phase CSC9 inverter-based PV system, is designed to extract maximum power from the PV panels and feed it to the grid with unity power factor and low current total harmonic distortion (THD). These objectives are achieved while regulating the CSC capacitor's voltage at its reference value to maintain the 9 voltage levels at the inverter output terminals. In the cost function design, the minimization of the switching transitions along with the errors on the capacitor voltage and grid current (consequently current THD) were selected as control objectives, where the higher priority was given to the latter. The objective of reducing the switching events is assumed to have the least priority while the weighting factor on the capacitor voltage is dynamic and its value is determined by a pre-defined polynomial. Moreover, the optimization of the cost function is performed over two-step prediction horizon. The performance of the proposed control design (current THD enhancement and switching transitions reduction) is compared in simulation with the fixed weighting factor and one-step horizon without switching transitions constraint FCS-MPC case scenario.

This paper proposes an auto-tuned finite control set-model predictive control (FCS-MPC) for a grid-tied singlephase crossover switches cell (CSC) inverter. The multilevel inverter (MLI) under study generates 9 voltage levels. The FCSMPCobjective is to minimize the total harmonic distortion (THD) of the current fed to the grid with unity power factor while regulating the capacitor voltage at its reference value to maintain the 9 voltage levels. The switching losses are reduced by managing the redundant switching states selection. Artificial Neural Network (ANN) based on the Bayesian regularized feedforward learning technique is applied to predict the optimal weighting factor of the FCS-MPC with respect to the measured reference current value. The effect of using a dynamic weighting factor on the current THD for different reference current peak values (ranging from 2A to 8A) is studied through MATLAB/Simulink simulation. The presented simulation is intended to show that the application of a dynamic weighting factor can significantly enhance the current THD compared to the use of a fixed weighting factor.

The Crossover Switches Cell (CSC) is a recent Single DC-Source Multilevel Inverter (SDCS-MLI) topology with boosting abilities. In grid-connected PV applications, the CSC should be controlled to inject a sinusoidal current to the grid with low THD% and unity power factor, while balancing the capacitor voltage around its reference. These two objectives can be met through the application of a finite control set model predictive control (FCS-MPC) method. Thus, this paper proposes a design of an optimized FCS-MPC for a 9-level grid-tied CSC inverter. The switching actions are optimized using the redundant switching states. The design is verified through simulations and real-time implementation. The presented results show that the THD% of the grid current is 1.73%, and the capacitor voltage is maintained around its reference with less than 0.5 V mean error. To test the reliability of the control design, different scenarios were applied, including variations in the control reference values as well as the AC grid voltage. The presented results prove the good performance of the designed controller in tracking the reference values and minimizing the steady-state errors.

Having a reduced number of switches and isolated DC sources yet generating a higher number of voltage levels has been always the challenge when selecting the appropriate Multilevel Inverter (MLI) topology. Nowadays, Single-DC-Source Multilevel Inverter (SDCS-MLI) topologies are being considered as more suitable for many power system applications such as Renewable Energy (RE) conversion systems and electrified transportations compared to the Multiple-DC-Source MLIs (MDCS-MLIs). Moreover, increasing the power rating and minimizing the switching frequency while maintaining reasonable power quality using a SDCS-MLI is an important requirement and a persistent challenge for the industry. Thus, this paper presents a general review on the available SDCS-MLI topologies and future trends. Existing solutions are discussed and analyzed based on their topologies, number of output voltage levels, number of active/passive components, advantages/limitations, maturity, and industrial applications. Furthermore, recommendations for future research and development are suggested in this paper.

In this paper, a finite-control-set model predictive controller (FCS-MPC) is designed for a grid-connected single-phase crossover switches cell (CSC) multilevel inverter (MLI). The CSC inverter is a modified 3-cell packed-U-cells (PUC) MLI by adding two crossover switches between the two DC-link and the flying capacitor. This modification allows the CSC to generate an output voltage with a peak value greater than the value of the DC source voltage (boost capability). The proposed FCS-MPC is designed, by selecting the appropriate weighting factor values for different design parameters, to inject current to the grid at unity power factor and low total harmonic distortion (THD) while regulating the capacitor voltage at the reference level. The proposed FCS-MPC is validated in simulation where the presented results show a high dynamic performance and capability of dealing with a multi-objective control problem at different operating conditions.