Research on parameter optimization design of coreless transformer based on genetic algorithm.

This study found that the inter-coil spacing (H) of a coreless transformer, the operating frequency (f0), the load (RL) on the transformer's secondary circuit, and the actual operating frequency (f) all affect the transmission efficiency (η) of the transformer. To address this, a random forest regression model was used to predict the transmission efficiency (η) as a function of the parameters (f0, H, RL, f). A genetic algorithm was employed to optimize the parameters (f0, H, RL, f) in order to maximize η. Based on the optimization results, efficient transformer and circuit designs were developed. After simulation verification, a prototype circuit was fabricated and tested. The results showed that the transmission efficiency of the planar coreless transformer, optimized using the genetic algorithm, reached 71.7%, a 54.8% improvement over the unoptimized circuit and a 17.7% increase compared to the directly designed transformer circuit without optimization. This demonstrates the effectiveness of the proposed optimization approach.