This paper presents a groundbreaking control strategy for a bidirectional battery charger that allows power to be injected into the smart grid while simultaneously compensating for the grid’s reactive power using an electric vehicle battery. An artificial neural network (ANN) controller is utilized for precise design to ensure optimal performance with minimal error. The ANN technique is applied to generate sinusoidal pulse width modulation (SPWM) for a bidirectional AC–DC inverter, with the entire algorithm simulated in MATLAB Simulink.The core innovation of this study is the creation of the ANN algorithm, which supports grid compensation using electric vehicle batteries, an approach termed “vehicle-for-grid”. Additionally, the paper details the PCB circuit design of the system controlled by the DSP F28379D board, which was tested on a three-phase motor. The total harmonic distortion (THD) of the proposed ANN algorithm is approximately $1.85\%$, compared to the MPC algorithm’s THD of about $2.85\%$. This indicates that the proposed algorithm is more effective in terms of the quality of the power injected into the grid. Furthermore, it demonstrates effective grid compensation, with the reactive power effectively neutralized to $0\mathrm{KVAR}$ in the vehicle-for-grid mode.

This paper investigates optimizing the power exchange between electric vehicles (EVs) and the grid, with a specific focus on the DC-DC converters utilized in vehicle-to-grid (V2G) systems. It specifically explores using model predictive control (MPC) in synchronous boost converters to enhance efficiency and performance. Through experiments and simulations, this paper shows that replacing diodes with SIC MOSFETs in boost converters significantly improves efficiency, particularly in synchronous mode, by minimizing the deadtime of SIC MOSFETs during switching. Additionally, this study evaluates MPC’s effectiveness in controlling boost converters, highlighting its advantages over traditional control methods. Real-world validations further validate the robustness and applicability of MPC in V2G systems. This study utilizes TMS320F28379D, one of Texas Instruments’ leading digital signal processors, enabling the implementation of MPC with a high PWM frequency of up to 200 MHz. This processor features dual 32-bit CPUs and a 16-bit ADC, allowing for high-resolution readings from sensors. Leveraging digital signal processing technologies and advanced electronic circuits, this study advances the development of high-performance boost converters, achieving power outputs of up to 48 watts and output voltages of 24 volts. Electronic circuits (PCB boards) have been devised, implemented, and evaluated to showcase their significance in advancing efficient V2G integration.

Photovoltaic (PV) systems experience significant power losses under partial shading conditions (PSCs) due to mismatched module outputs, limiting maximum power extraction. To address this issue, a hybrid maximum power point tracking (MPPT) algorithm, artificial neural network–Grey wolf optimization (ANN–GWO), is introduced, combining ANNs and GWOs. ANN reduces tracking time to approximately 0.02 seconds during rapid weather fluctuations, while GWO enhances power extraction under severe shading conditions. On the grid side, model predictive control (MPC) optimizes single-phase inverter operation, ensuring stable grid integration and efficient power transfer for residential and microgrid-based electric vehicle (EV) charging. This approach improves dynamic tracking efficiency by over 9% and reduces MPPT tracking time by up to 99.98% compared to conventional methods. Additionally, MPC lowers total harmonic distortion (THD) from 2.56% to 1.56%, enhancing power quality and response time. Implemented on the Texas Instruments TMS320F28379D DSP, the system ensures fast and stable power tracking, outperforming traditional control methods. Both simulations and real-world experiments validate the proposed system, demonstrating significant advancements in PV-based EV charging performance. This study focuses on developing a Hybrid ANN–GWO MPPT combined with MPC-based inverter control to enhance PV-powered EV charging under PSC, aiming to improve tracking efficiency, reduce THD, and implement a real-time DSP-based experimental setup.

Grid-forming inverters (GFIs) are emerging as a key enabling technology for maintaining stability in renewable-dominated power systems, where conventional synchronous generation is progressively displaced by inverter-based resources. This paper presents a comprehensive technical review of GFI control strategies applied to photovoltaic (PV) systems, with focused attention on small-signal stability, transient dynamic performance, and overcurrent-limiting capabilities. In contrast to grid-following inverters (GFLIs), which rely on phase-locked-loop synchronization, GFIs operate as voltage sources capable of forming and regulating grid voltage and frequency. The reviewed control approaches, including droop control, virtual synchronous generator (VSG), synchronverter, matching control, virtual oscillator control (VOC), model predictive control (MPC), and intelligent techniques such as fuzzy logic control (FLC), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFISs), are systematically compared based on dynamic response characteristics, robustness under weak-grid conditions, control complexity, and practical implementation challenges. The paper synthesizes recent findings on stability margins, inertia emulation, transient current response, and protection requirements, highlighting remaining research gaps related to large-disturbance ride-through capability, coordination of multiple GFIs, and protection integration. These insights aim to support future deployments of reliable grid-forming photovoltaic systems in resilient inverter-dominated power networks.

This paper presents the development of an intelligent battery charging system for electric vehicle (EV) charging stations, incorporating a Photovoltaic (PV) system with a buck converter to improve power transfer efficiency in partial shade scenarios. The intrinsic unpredictability of solar irradiation causes changes in photovoltaic output power, requiring an intelligent optimization strategy. The study utilizes an Enhanced Particle Swarm Optimization (E-PSO) algorithm, a stochastic search method, to optimize power extraction and enhance overall system performance. The DSP F28379D microcontroller is employed for real-time execution, producing high-frequency pulse-width modulation (PWM) signals for accurate regulation of the buck converter. Experimental validation confirms the enhanced performance of the proposed E-PSO algorithm, with a response time of 0.04 s and an efficacy of 99.90%, markedly decreasing charging duration and improving power output relative to traditional MPPT techniques. These findings highlight the effectiveness and feasibility of the proposed method for efficient and adaptive electric vehicle battery charging in variable operating conditions.