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.

Brushless DC (BLDC) motors require accurate rotor position feedback to guarantee reliable electronic commutation. However, hall-effect sensor signals are often degraded by high-frequency switching noise from the inverter, which can cause false commutations and control errors. Moreover, a direct connection to control hardware may introduce ground loops and jeopardize sensitive electronics. This study proposes a hardware-based hall signal conditioning method that integrates RC low-pass filters, designed with a 1.59 kHz cutoff frequency, to attenuate inverter-induced noise, and 4N35 optocouplers to provide galvanic isolation. Unlike existing approaches that rely primarily on algorithmic noise rejection or digital filtering, the proposed solution offers a compact, low-latency hardware implementation suitable for real-time embedded control. Experimental validation using a dSPACE DS1104 board shows a 14.7 dB improvement in signal-to-noise ratio (SNR) and a 36% reduction in timing jitter, ensuring clean and isolated hall signals for stable six-step commutation. These improvements directly translate into smoother torque production, enhanced speed stability, and increased protection of control electronics, making the method applicable to both research and industrial BLDC motor systems operating in noisy environments.

Wireless chargers for electric vehicles have recently attracted considerable attention from researchers and scientists, particularly due to their promising applications for high-power transfer, such as in heavy-duty vehicles, which are the backbone of transportation. This paper presents an innovative method for controlling a threephase magnetic resonance wireless power transfer charger using genetic algorithm optimization of parameters to ensure efficient wireless energy transfer. This control method, which operates exclusively on the primary side, regulates the battery current in the case of charging using the constant current-constant voltage method and also in the case of charging using the multi-step current method, which preserves the state of health of the battery. The state of charge (SOC) of the battery is estimated by neural networks, which ensure SOC estimation despite communication loss. The proposed control method is tested under several scenarios for a wireless power transmission of 20 kW with a resonance frequency of 85 kHz and an efficiency of 90.15%. The results obtained demonstrate the robustness and effectiveness of the proposed method for transferring wireless power to heavyduty vehicles despite misalignment between the coils and variation in the air gap, two frequent and disruptive phenomena in real-world situations.

Experimental validation is essential in renewable energy emulators and energy storage systems to ensure reliability, accuracy, and practical implementation. However, the high cost of AC/DC converters, measurement circuits, and microcontroller-based control platforms limits access to experimental validation, particularly in developing regions. This paper proposes cost-effective hardware and software solutions for validating renewable energy systems and storage management. The proposed approach is based on affordable power converters, low-cost voltage and current measurement circuits, data acquisition tools, and open-source control platforms. The methodology includes the design, implementation, and experimental testing of these low-cost solutions under different operating conditions. The obtained results demonstrate satisfactory measurement accuracy, stable behavior, and acceptable error margins, confirming the feasibility of the proposed setup for experimental validation. These solutions provide a scalable and accessible alternative to expensive laboratory platforms, enabling researchers and institutions with limited resources to perform reliable experimental studies and contribute to the advancement of sustainable energy technologies.