Temperature-Compensated Multi-Objective Framework for Core Loss Prediction and Optimization: Integrating Data-Driven Modeling and Evolutionary Strategies

Yong Zeng, Da Gong (), Yutong Zu and Qiong Zhang
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Yong Zeng: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Da Gong: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Yutong Zu: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Qiong Zhang: State Key Laboratory of Deep Earth Exploration and Imaging, School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China

Mathematics, 2025, vol. 13, issue 17, 1-31

Abstract: Magnetic components serve as critical energy conversion elements in power conversion systems, with their performance directly determining overall system efficiency and long-term operational reliability. The development of accurate core loss frameworks and multi-objective optimization strategies has emerged as a pivotal technical bottleneck in power electronics research. This study develops an integrated framework combining physics-informed modeling and multi-objective optimization. Key findings include the following: (1) a square-root temperature correction model (exponent = 0.5) derived via nonlinear least squares outperforms six alternatives for Steinmetz equation enhancement; (2) a hybrid Bi-LSTM-Bayes-ISE model achieves industry-leading predictive accuracy (R 2 = 96.22%) through Bayesian hyperparameter optimization; and (3) coupled with NSGA-II, the framework optimizes core loss minimization and magnetic energy transmission, yielding Pareto-optimal solutions. Eight decision-making strategies are compared to refine trade-offs, while a crow search algorithm (CSA) improves NSGA-II’s initial population diversity. UFM, as the optimal decision strategy, achieves minimal core loss (659,555 W/m 3 ) and maximal energy transmission (41,201.9 T·Hz) under 90 °C, 489.7 kHz, and 0.0841 T conditions. Experimental results validate the approach’s superiority in balancing performance and multi-objective efficiency under thermal variations.

Keywords: core loss; machine learning; Steinmetz modified equation; Bi-LSTM-bayes-ISE; multi-objective optimization (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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