Physics-Informed Neural Network Model Description for an SIMO Bidirectional Resonant Converter for Wide-Load Applications

This paper presents a Physics-Informed Neural Network (PINN) modeling approach for power converters with a high number of degrees of freedom. In contrast to traditional modulation strategies, which depend on harmonic approximations or time-domain analysis, the proposed method accounts for duty cycles, phase shifts, and input/output power relationships to help identify more efficient operating points. A resonant multi-output converter is used as a case study, featuring a five-level T-inverter on the primary side and a cascaded two-cell multilevel inverter on the secondary side. This topology maintains isolation between the high-voltage input and low-voltage outputs, and the cascaded structure reduces transformer turns, leading to possible improvements in power density. A particle swarm optimization (PSO) algorithm is applied to the PINN-predicted data to optimize performance further and identify optimal parameter combinations. A low-power prototype is implemented to validate the approach, demonstrating efficiency gains under light-load conditions. The results highlight the potential of AI-driven modeling and optimization in extending converter efficiency across diverse operating scenarios. Trade-offs, limitations, and future research directions are discussed, including digital twin integration and application to other topologies.