An ANN-Based System for Lateral Misalignment and Vertical Clearance Estimation of an Electric Vehicle During Dynamic Wireless Charging

Due to decaying fossil fuel reserves, oil price fluctuation and detrimental effects on climate the use of fossil fuel has, people are getting more interested in other alternative energy sources. In the past couple of decades, the Electric Vehicles have emerged as a robust environment-friendly alternative to conventional gasoline-driven vehicles. Although EVs have a problem with the limited energy storage, Stationary and Dynamic Wireless Power Transfer (WPT) systems for the charging of EVs can be an effective solution. However, in a WPT system, energy efficiency and energy transfer capability are significantly affected by the level of Lateral Misalignment. The real-time estimation of LTM, followed by some corrective actions, could result in better energy efficiency and the power transfer capability of the system. This report describes the theory, design, and simulation of an Artificial Neural Network based system for predicting LTM and Vertical Clearance of EVs, so that the LTM can be corrected to optimize Wireless Power Transfer for EVs