Enhancing Electric Vehicle Charging with Dynamic Adaptation: A Machine Learning Approach to Improving Grid Alignment Precision

This study introduces an advancement in the Grid Alignment Assistant System (GAAS) by integrating machine learning to dynamically adapt to varying road conditions, vehicle speeds, and charging grid configurations. This upgrade enables the system to learn from its environment and past alignment experiences, significantly improving its precision over time. By leveraging real-time data and predictive analytics, the system can adjust its algorithms to optimize alignment accuracy, ensuring efficient wireless charging for electric vehicles under diverse operational conditions. This novel approach not only enhances the GAAS's adaptability and performance but also paves the way for more autonomous and intelligent charging solutions in the evolving landscape of electric mobility.