Research on DAB output current sharing and multi-objective optimization under parameter drift
Dual active bridge (DAB) converters are pivotal in applications requiring bidirectional power flow, such as automotive and aerospace systems. The input series and output parallel (ISOP) configuration addresses the need for higher output currents by reducing the voltage stress on individual modules, yet it faces challenges like slow dynamic response, parameter sensitivity, and difficulties in maintaining output current sharing amid component mismatches and input disturbances. This study establishes an evaluation framework to quantify current-sharing risks in ISOP-DAB systems under extended phase shift (EPS) modulation, analyzing how inductance discrepancies and input voltage dispersion propagate uncertainties and define risk boundaries. By developing a parameter-uncertainty model and applying a multi-objective genetic algorithm, the research concurrently optimizes inductor current stress and the reflux of power. MATLAB simulations validate the theoretical derivations, demonstrating effective current sharing and enhanced performance under multivariable uncertainties. The conclusions confirm the framework's accuracy in guiding the robust design of ISOP-DAB converters, achieving a balance between operational stability and efficiency.