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.

This paper addresses the control task of a wireless power transfer (WPT) charger designed for electric vehicles (EVs). The challenge is to maintain a constant battery charging current when the WPT is controlled on the ground side. Indeed, the intermittent latency involved in the wireless data communication between the ground and vehicle sides leads to system instability. To overcome this issue, a new control approach has been proposed in this paper. The proposed technique ensures indirect control of the battery charging current through control of the current on the ground side. The control technique relies on an adaptive hill-climbing algorithm in conjunction with a PI-based controller. The adaptive parameter is adjusted online, during the operation of the charger, only when a new measure of the battery charging current is received on the primary side. This makes it possible to avoid the need for real-time wireless data communication. It should be noted that this aspect is crucial in ensuring the controller’s robustness and stability of the system regardless of potential delays in wireless communication and large misalignments between the coils. The validity of the proposed control technique has been confirmed through simulation. In addition, experimental validation, using a laboratory test bed, demonstrated satisfactory results.

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.

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 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.

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.