Abstract
Air supply system is an important subsystem in a PEMFC engine system. Research on the control strategy of air supply system is of great importance and significance in engineering. In this paper an intelligent controller based on distributed deep reinforcement learning which exerts better control over the air flux of a proton exchange membrane fuel cell (PEMFC) air supply system is proposed. In addition, a collective intelligence exploration distributed multi-delay deep deterministic policy gradient (CIED-MD3) algorithm is presented for the controller. This improved algorithm is developed on the basis of deep deterministic policy gradient (DDPG) and adopted the collective intelligence exploration policy which enables full exploration of the environment. This classification experience replay mechanism is introduced to improve training efficiency. A number of techniques are employed in an effort to address the Q-value overestimation problem of the DDPG, including clipped multiple Q-learning, delayed update of policy and smooth regularization of target policy. Finally, the application of CIED-MD3 (with its better global search ability and optimization speed) is demonstrated to the model-free PEMFC air flux intelligent controller. The simulation results show that the proposed controller exerts greater control of the PEMFC air supply system. Compared with other control methods, the proposed intelligent controller exhibits better control performance and robustness. The control algorithm proposed in this paper is of significance to future PEMFC air flux control research.
Highlights
There has been growing application of fuel cell (FC) across multiple industries in recent years in response to declining fossil fuel reserves, as well as worsening trends in environmental pollution and climate change
The cathode air flux of the proton exchange membrane fuel cell (PEMFC) requires quick and accurate control in order for it to respond to a change of load in a timely manner according to different power demands [4]
Lower air flux can result in insufficient oxygen supply, which will result in a reduced stack output voltage; a larger cathode air flux will lead to an increase in parasitic power consumption in the air supply system [5]
Summary
There has been growing application of fuel cell (FC) across multiple industries in recent years in response to declining fossil fuel reserves, as well as worsening trends in environmental pollution and climate change. To improve PEMFC’s performance in controlling air flux, this paper proposes the design of a controller which is based on CIED-MD3. (1) This paper proposes an intelligent controller framework on the basis of deep reinforcement learning, which has better adaptivity, control performance and robustness. The CIED-MD3 algorithm with better global search ability and optimization speed is applied to the modelfree PEMFC air flux intelligent controller.
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