Improved Control of DC-DC Converter for Fuel Cell and Battery Hybrid System Based on Ant Colony Optimization and RL-TD3 Agent

Based on the increasing use of a Proton Exchange Membrane-Fuel Cell (PEM-FC) stack, and starting from a benchmark that has such a power generation structure including a battery, this article presents the global architecture of the proposed system and its main components, where the general objective is to keep the V
DC
voltage of the DC type circuit constant. Due to the long response time of the PEM-FC stack for internal thermodynamic reasons, in the proposed system, we present the improvement of the performance for the Proportional Integral (PI) controller of the DC-DC converter by using a Computational Intelligence-Ant Colony (CI-ACO) algorithm for obtaining the optimal values of the tuning parameters for the PI controller, but also by using a Reinforcement Learning -Twin-Delayed Deep Deterministic Policy Gradient (RL-TD3 agent), which through the correction signals provided contributes to obtaining superior control performance. The comparative verification of the performance for the proposed control systems was performed in the Matlab/Simulink programming environment.