Research on Neural Network Global Optimization of Hybrid Full-Bridge Push-Pull Topology Based on Genetic Algorithm

The traditional control strategies for bidirectional power supply full-bridge push-pull DC-DC topologies still face limitations in efficiency, dynamic response, and output stability. To address this, this paper proposes an integrated modulation strategy combining neural network optimization and closed-loop control, which adjusts the phase-shift angle and switching timing through online learning to significantly improve dynamic and steady-state performance. Simulations show that the current peak value was reduced from 16A to 15.2A, the output voltage ripple was significantly suppressed from 90% to 30%, and the system efficiency, calculated through multiple iterations, gradually increased. This paper first analyzes the problems of traditional control strategies, then presents a new control framework, modeling, and simulation. Finally, simulation verification was performed under typical operating conditions. The results show that this strategy is suitable for high-efficiency energy storage systems.