A Data-Driven Approach for Magnetic Core Loss Modeling and Multi-Condition Performance Optimization

Magnetic core loss is a pivotal factor influencing the efficiency and performance of magnetic components in power electronics systems. Traditional empirical models, such as the Steinmetz equation, often fall short in accurately predicting core loss under complex and varying operational conditions due to their inability to capture non-linear relationships and interactions among multiple factors. This study addresses these limitations by developing a high-precision prediction model for magnetic core loss using advanced machine learning techniques. Through comprehensive feature engineering, critical time-domain and frequency-domain features are extracted from magnetic flux density data, enabling effective waveform classification and loss prediction. An ensemble learning approach, which integrates multiple regression models, is employed to enhance prediction accuracy and robustness. Additionally, a multi-objective optimization framework utilizing genetic algorithms is implemented to simultaneously minimize core loss and maximize magnetic energy transfer, represented by the product of frequency and magnetic flux density peak. Experimental results demonstrate that the ensemble model achieves exceptional performance with a Root Mean Squared Error (RMSE) of 9619.38 and a coefficient of determination ($R^{2}$) of 0.9993. The optimization process successfully identifies optimal conditions that balance core loss minimization and energy transfer maximization, providing valuable insights for the design and enhancement of magnetic components. This integrated approach not only improves prediction accuracy but also offers a scalable and effective solution for optimizing magnetic component performance in applications such as renewable energy systems, electric vehicles, and communication infrastructures. The findings underscore the potential of machine learning and genetic algorithms in advancing the design and efficiency of power electronic systems.