Modeling of Dual Active Bridge Converter with Data Pruning for Optimal Current Stress Performance Under EPS Modulation

The modeling and design of the dual active bridge converter modulation strategy are essential to achieve comprehensively optimal performance in practical applications. The commonly used modeling approaches include knowledge-based and emerging data-based methods. Despite significantly reducing the required domain expertise and analytical complexity, the data-based approach heavily relies on the quality of the collected data. Therefore, this research focuses on removing abnormal data points and ensuring data quality for the data-based modeling approach. In this paper, a data-based modeling method with data pruning (MIDI) is proposed, which employs one-class support vector machine for data preprocessing before training the surrogate model. This approach has been validated with a design case and 1kW prototype hardware experiments.