Analytical–Model–Based Global Search Optimization Algorithm for Doubly Current-Fed Dual Half-Bridge Converters

This paper presents an analytical-model-based global search algorithm for determining the optimal modulation parameters of non-resonant dual half-bridge converters. Based on a derived general piecewise-linear analytical model, rather than an approximate one, such as the fundamental harmonic model, the method accurately captures the relationships between the modulation parameters and key converter electrical variables across the entire operating range. Furthermore, the proposed method prevents convergence to local extrema and achieves true global optimal modulation parameters, with the objective of minimizing the transformer RMS current while satisfying constraints on electrical variables, e.g., ensuring that the bridge-arm voltage remains within its allowable limits to guarantee safe converter operation. The obtained optimal modulation parameters can be stored in look-up tables or approximated using fitting functions. These parameters can then be applied in circuit simulations or loaded into the controller to operate the hardware converter. Both comparative simulations and experimental results, using a doubly current-fed dual half-bridge (CF2DHB) converter as an example, validate the accuracy of the derived analytical model, the effectiveness of transformer RMS current reduction, and the resulting efficiency improvement. In addition, the proposed optimization method can potentially be applied to various dual half-bridge converter topologies, leading to enhanced performance in a wide range of applications.