Modeling and Multi-Objective Optimization of Transcutaneous Energy Transmission Coils Based on Artificial Intelligence

This paper proposes a machine learning-based modeling and multi-objective optimization method for transcutaneous energy transfer coils to address the problem that current transcutaneous energy transfer coils with single-objective optimization design methods have difficulty achieving optimal solutions. From modeling to multi-objective optimization design, the whole transcutaneous energy transfer coil process is covered by this approach. This approach models transcutaneous energy transfer coils using the Extreme Learning Machine, and the Gray Wolf Optimization algorithm is used to tune the Extreme Learning Machine’s parameters in order to increase modeling accuracy. The Non-Dominated Sorting Whale Optimization algorithm is utilized for multi-objective optimization of the transcutaneous energy transfer coils, which is based on the established model. Using the optimization of planar helical coils applied in artificial detrusors as an example, a verification analysis was conducted, and the final optimization analysis results were demonstrated. The results indicate that the Gray Wolf Optimization algorithm significantly outperforms the comparison algorithms in tuning the parameters of the Extreme Learning Machine model, and it exhibits good convergence ability and stability. The established transcutaneous energy transfer coil prediction model outperforms the comparative prediction model in terms of evaluation metrics for predicting the three outputs (transmission efficiency, coupling coefficient, and secondary coil diameter), demonstrating excellent prediction performance. The Non-Dominated Sorting Whale Optimization algorithm performs well in the multi-objective optimization process of transcutaneous energy transfer coils, showing excellent results. The Pareto optimal solutions obtained using this algorithm have errors of 3.03%, 0.1%, and 1.7% for transmission efficiency, coupling coefficient, and secondary coil diameter, respectively, when compared to the simulation and experimental calculations. The small errors validate the correctness and effectiveness of the proposed method.