Integrating electric vehicles (EVs) into the transportation ecosystem is crucial for environmental protection. With the increasing demand for sustainable mobility solutions, wireless power transfer (WPT) systems present a promising method to facilitate the adoption of EVs while reducing carbon footprints. This paper presents a control strategy for the primary side of a WPT charger utilizing a genetic algorithm (GA) combined with a feedforward artificial neural network (ANN). The aim is to optimize charging in constant current (CC) mode and enhance energy transmission efficiency. The proposed approach employs a GA to control the WPT charger, enabling real-time adaptation of charging parameters. The ANN estimates the system’s efficiency, ensuring optimal performance during the charging process. The developed control strategy significantly improved energy transfer efficiency and system stability. Simulation results demonstrate the effectiveness of this new approach, achieving an efficiency of 89.32% in challenging situations of loss of communication with the vehicle. To validate the design procedure, an experimental prototype was constructed, operating at an operational frequency of 85 kHz. Experimental results confirm the proposed design methodology.

Today, hydrogen fuel cells occupy a crucial position in sustainable energy systems. However, a precise model of their performance is needed to improve their efficiency and integrate them into hydrogen electric vehicles. This paper presents a hydrogen fuel cell model based on artificial neural networks (ANNs) to predict its performance characteristics. Using experimental data from a PEMFC NEXA 1200 hydrogen fuel cell in the ISA laboratory, an ANN model optimized by deep learning was developed, integrating advanced training techniques. The model’s performance was evaluated on independent test sets, revealing predictive precision with a low mean squared error (MSE) of 0.0429, a low Mean Absolute Percentage Error (MAPE) of 1.05%, a low Root-Mean-Square Error (RMSE) of 0.2071, and a high coefficient of determination (R²) of 0.9071. The model’s development and evaluation will be reviewed here in order to visualize the training progress and the results of the simulation. The main advantages of the proposed ANN model lie in both its flexible architecture, which can capture complex relationships without the need for explicit physical models, and its predictive and optimization capability.

This paper presents a hybrid fast charging strategy for static wireless power transfer (WPT) systems that synergistically combines pulsed current and multi-stage current (MCM) modes to enable rapid yet battery-health-conscious electric vehicle (EV) charging, thereby promoting sustainable transportation. The proposed approach employs a memetic algorithm (MA) to dynamically optimize the charging parameters, achieving an optimal balance between speed and battery longevity while maintaining 90.78% system efficiency at the SAE J2954-standard 85 kHz operating frequency. A neural-network-based state of charge (SOC) estimator provides accurate real-time monitoring, complemented by MA-tuned PI control for enhanced resonance stability and adaptive pulsed current–MCM profiles for the optimal energy transfer. Simulations and experimental validation demonstrate faster charging compared to that using the conventional constant current–constant voltage (CC-CV) methods while effectively preserving the battery’s state of health (SOH)—a critical advantage that reduces the environmental impact of frequent battery replacements and minimizes the carbon footprint associated with raw material extraction and battery manufacturing. By addressing both the technical challenges of high-power WPT systems and the ecological imperative of battery preservation, this research bridges the gap between fast charging requirements and sustainable EV adoption, offering a practical solution that aligns with global decarbonization goals through optimized resource utilization and an extended battery service life.

This paper presents an advanced Adaptive Sliding Mode Control (ASMC) strategy, specifically developed for a hydrogen production system based on a Proton Exchange Membrane electrolyzer (PEM electrolyzer). This work utilized a static model of the PEM electrolyzer, characterized by its V-I electrical characteristic, which was approximated by a linear equation. The ASMC was designed to estimate the coefficients of this equation, which are essential for designing an efficient controller. The primary objective of the proposed control strategy is to ensure the overall stability of the integrated system comprising both an interleaved buck converter (IBC) and PEM electrolyzer. The control framework aims to maintain the electrolyzer voltage at its reference value despite the unknown coefficients while ensuring equal current distribution among the three parallel legs of the IBC. The effectiveness of the proposed approach was demonstrated through numerical simulations in MATLAB-SIMULINK and was validated by the experimental results. The results showed that the proposed ASMC achieved a voltage tracking error of less than 2% and a current distribution imbalance of only 1.5%. Furthermore, the controller exhibited strong robustness to parameter variations, effectively handling fluctuations in the electrolyzer’s ohmic resistance (R_ohm) (from ±28.75% to ±40.35%) and in the reversible voltage (E_rev) (from ±28.67% to ±40.19%), highlighting its precision and reliability in real-world applications.

Precise voltage and current regulation is essential to ensure the required power output and maximum efficiency in charging stations, particularly those utilizing Wireless Power Transfer (WPT) systems. Effective regulation techniques are necessary to manage voltage and current in Battery Electric Vehicles (BEVs) operating under various charging modes. This study outlines the controller design for different methods for charging lithium-ion batteries in a WPT charger. Initially, the fundamental concepts of WPT systems and their equivalent circuit are introduced. Subsequently, the control strategy for current regulation is detailed for the Constant Current (CC) mode, the Multi-stage Current Method (MCM), and the Pulse Charging Method (PCM). Finally, the resilience and validity of this innovative approach to controlling various techniques for charging lithium-ion batteries are demonstrated through simulations.

This paper investigates a new approach to control a wireless power transfer (WPT) charger for electric vehicles (EVs) employing an optimized artificial neural network (ANN). Enhancing the efficiency and robustness of such systems is crucial, and integrating artificial intelligence (AI)-based solutions has introduced innovative approaches in this field. The proposed method enables precise regulation of battery charging voltage even under challenging conditions, such as coil misalignment or shared grounding assemblies for multiple EVs. To assess the stability and robustness of the proposed controller, its performance was evaluated under scenarios of coil misalignment and shared grounding assemblies for EVs with varying battery voltages. The controller effectively eliminated overshoot and significantly reduced residual output voltage ripple by 4.33% compared to a conventional proportional-integral (PI) controller, demonstrating the superior performance and reliability of the ANN-based control approach.