Machine Learning-Driven Joint Structuring of WPT Coil and Core for Enhanced Mutual Inductance and Reduced Ferrite Volume

Machine learning (ML) algorithms have shown promise in optimizing wireless power transfer (WPT) systems, particularly for electric vehicle charging and medical implants. However, most approaches focus on isolated WPT components, limiting their overall impact. This study presents an integrated optimization of WPT coil and core designs to enhance mutual inductance between transmitting (Tx) and receiving (Rx) coils. We propose a hybrid residual layer sequential neural network (HRL-SeqNet), along with SVR-based multivariate regression and deep Qnetwork (DQN) models, to enhance the WPT performance. HRL-SeqNet incorporates GRU, BiLSTM, and LSTM RNNs followed by a second-level GRU and ensemble regression to optimize the copper windings, achieving a high mutual inductance of 11.284 μH with a conventional ferrite core. Additionally, SVR and DQN models optimize the ferrite core by introducing asymmetric and symmetric vacuum configurations. The SVR-optimized asymmetric core achieved a 0.585% increase in mutual inductance while reducing ferrite volume by 24.49%. In addition, the DQN model increased mutual inductance by 1.38% and 3.16% for order 4 and order 8 rotational symmetry, respectively, with corresponding reductions in ferrite volume of 23.47% and 25.51%. These ML models show exceptional computational efficiency, with HRL-SeqNet, SVR, and DQN achieving 4,300x, 16,960x, and 10,138x speedups, respectively, over ANSYS Maxwell simulations. Experimental validations confirm the effectiveness of these ML-optimized designs, demonstrating their applicability across various WPT applications.