Data-Efficient Multi-Objective Design of Auxiliary Localization Coils for Misalignment-Robust UAV WPT

To address the challenges of difficult quantitative design and potential coil mismatch in auxiliary coils within wireless power transfer systems, a data-driven parameter optimization method based on multi-objective particle swarm optimization (MOPSO) was proposed. First, based on the inductor–capacitor–capacitor series (LCC-S) compensation topology, a mechanism-based analysis was conducted, establishing coil side length A and number of turns N as core optimization variables. Subsequently, a collaborative optimization framework integrating “parametric simulation–surrogate modeling–active learning” was established. An offline fingerprint database was constructed via finite element simulation, and a high-accuracy surrogate model was developed using a kernel ridge regression ensemble approach. Active learning strategies were employed to adaptively augment data points and mitigate uncertainty. Finally, the multi-objective particle swarm optimization (MOPSO) algorithm was applied to identify the Pareto-optimal solution set. Experimental results reveal that the optimized auxiliary coil parameters achieved positioning errors below 8 mm at all test points. The maximum positioning error was significantly reduced by approximately 80% compared to the traditional empirical approach, providing a useful parameter-selection reference for high-precision wireless charging alignment systems under the investigated static operating conditions.