Hybrid ANN-GWO MPPT with MPC-based inverter control for efficient EV charging under partial shading conditions.

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.