Wireless power transfer (WPT) chargers are promising solutions for charging electric vehicles (EVs). Due to their advantages such as ease and safety of use, these chargers are increasingly replacing conductive ones. In this paper, we first provide a detailed analysis to illustrate the effect of varying parameters on the operation of the WPT charger. Secondly, we present the main design steps of the charger elements while respecting the recommendations of the SAEJ2954 standard in terms of operating frequency, efficiency and misalignments. Regarding the design of the ground-side and vehicle-side coils, we propose three different circular geometries whose parameters are determined using an iterative approach. The latter is compared with a finite element analysis performed under Ansys Maxwell software showing the convergence between theoretical calculations and the simulation results. Finally, an experimental prototype with a power of 500 W is realized. In addition, different test scenarios are performed to validate the proposed design approach. In this respect, an efficiency of 90% is obtained for a power of 500 W and a distance between coils of 125 mm. Moreover, the test of the charger in the most unfavorable operating case (misalignments of Δx = 70 mm, Δy = 10 mm and Δz = 150 mm) gives an efficiency of 83.5%, which remains above the limit of the SAEJ2954 standard.

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 article presents an in-depth study of a dynamic wireless power transfer (DWPT) system used to charge electric vehicles (EVs), with a focus on modeling and controlling a double-D (DD) coil structure. The chosen DD coil design improves energy transfer efficiency and minimizes mutual coupling between adjacent transmit coils, a common problem in dynamic applications. A comprehensive mathematical model is developed to account for the nonlinear dynamics of the system, i.e., when the vehicle is moving and misalignments and coupling variations occur. A robust nonlinear control method based on sliding mode control (SMC) is implemented to ensure stable operation and accurate regulation of the output voltage. The controller is tested in different scenarios where the vehicle speed changes, thus ensuring its robustness and stability under all operating conditions. Particular attention is paid to the critical transition zone, in which the receiver coil is placed between two transmitter coils in order to achieve minimal magnetic coupling. The simulation results demonstrate that the proposed controller offers a fast dynamic response (~0.07 s) and stable voltage tracking, even in the event of significant variations in mutual inductance and different EV movement speeds. These results confirm the effectiveness of the control approach and its potential for real-time charging of electric vehicles in large-scale DWPT applications.

In terms of their high efficiency and low environmental impact, proton exchange membrane fuel cells (PEMFC) are becoming increasingly essential in the development of hydrogen electric vehicles. Despite these advantages, optimizing hydrogen consumption remains difficult because of the highly nonlinear behavior of PEMFC systems and their sensitivity to variations in operating conditions. This article outlines an intelligent control approach based on extremum seeking control (ESC), based on an artificial neural network (ANN) model, to improve hydrogen utilization in hydrogen electric vehicles. Experimental data on current, voltage, and temperature are collected, preprocessed, and used to train the ANN model of the PEMFC. The ESC algorithm uses this predictive ANN model to adjust the fuel cell current in real time, ensuring voltage stability while reducing hydrogen consumption. The simulation results demonstrate that the ANN-based ESC system provides voltage stability under dynamic load variations and achieves approximately 2.7% hydrogen savings without affecting the experimental current profile, validating the efficacy of the suggested strategy for effective hydrogen management in fuel cell electric vehicles.