Physics-Informed Neural Network based Modeling of Magnetic Couplers for EV Wireless Charging System

The design of magnetic couplers is crucial to the efficiency of inductive wireless power transfer (WPT) systems in EV charging application, where coil geometries and material layouts must be engineered under tight electromagnetic and packaging constraints. Conventional finite-element modeling (FEM) provides high fidelity but is computationally expensive for iterative design, while purely data-driven surrogate models are fast but often neglect electromagnetic constraints and material interactions. This paper presents a physics-informed neural network (PINN) framework that embeds the frequency-domain quasi-static A-form of Maxwell's equations with boundary and interface conditions, incorporates material-aware eddy-current effects, and leverages lightweight FEM-based calibration for absolute scaling. The method parameterizes DD-shaped and rectangular coils together with ferrite and aluminum shielding, trains a physics-constrained network on these inputs, and performs forward inference to directly predict inductive characteristics from geometry parameters. Validation on representative prototypes at 85 kHz shows agreement within a few percent relative to FEM and bench measurements, while reducing evaluation time from tens of minutes to a few minutes per design, thereby enabling rapid and accurate modeling of practical WPT couplers.