A data-driven output voltage control of solid oxide fuel cell using multi-agent deep reinforcement learning

To effectively control the output voltage of solid oxide fuel cells (SOFCs) and improve the operating efficiency of SOFC systems, an SOFC output voltage data-driven controller based on multi-agent large-scale deep reinforcement learning is proposed, whereby a discrete-continuous hybrid action space large-scale multi-agent twin delayed deep deterministic policy gradient (DHASL-MATD3) is used as the control algorithm for this controller. To solve the low robustness problem of deep reinforcement learning-based conventional controllers, this algorithm adopts a hybrid action space multi-agent policy that achieves parallel exploration by using double deep Qlearning (DDQN) agents with discrete space and deep deterministic policy gradient (DDPG) agents with continuous action space, thus improving exploration efficiency and realizing excellent robustness. In addition, many techniques are adopted by this algorithm to solve the problem of Q-value overestimation. Ultimately, an SOFC output voltage controller with stronger robustness is obtained. Simulation results show that this controller can effectively control the output voltage of a SOFC by regulating the fuel flux and maintaining its fuel utilization within a reasonable range.

Automated summary

This study proposes an SOFC output voltage controller based on multi-agent large-scale deep reinforcement learning, utilizing a hybrid action space control algorithm to effectively address the low robustness issue of conventional controllers, achieving stronger robustness. Simulation results demonstrate that regulating fuel flux within a reasonable range can effectively control the output voltage of SOFC.