Abstract
A data-driven optimal control method for an air supply system in proton exchange membrane fuel cells (PEMFCs) is proposed with the aim of improving the PEMFC net output power and operational efficiency. Moreover, a marginal utility-based double-delay deep deterministic policy gradient (MU-4DPG) algorithm is proposed as a an offline tuner for the PID controller. The coefficients of the PID controller are rectified and optimized during training in order to enhance the controller’s performance. The design of the algorithm draws on the concept of marginal effects in Economics, in that the algorithm continuously switches between different forms of exploration noise during training so as to increase the diversity of samples, improve exploration efficiency and avoid Q-value overfitting, and ultimately improve the robustness of the algorithm. As detailed below, the effectiveness of the control method has been experimentally demonstrated.
Highlights
Proton exchange membrane fuel cells (PEMFCs) convert hydrogen energy into electrical energy, and release heat energy directly via an electrochemical reaction (Sun et al, 2019; Yang et al, 2019)
A marginal utility-based double-delay deep deterministic policy gradient (MU-4DPG) algorithm is proposed as a tuner for the PID controller, one which is trained offline
The algorithm operates on the principle of marginal effects, a popular analytical concept in the field of Economics, by continuously switching the form of exploration noise in training in order to increase the diversity of samples, improve exploration efficiency and prevent Q-value overfitting, and improve the robustness of the algorithm to a better performing PID controller
Summary
Proton exchange membrane fuel cells (PEMFCs) convert hydrogen energy into electrical energy, and release heat energy directly via an electrochemical reaction (Sun et al, 2019; Yang et al, 2019). The algorithm operates on the principle of marginal effects, a popular analytical concept in the field of Economics, by continuously switching the form of exploration noise in training in order to increase the diversity of samples, improve exploration efficiency and prevent Q-value overfitting, and improve the robustness of the algorithm to a better performing PID controller. The innovations in this paper are as follows: 1) A data-driven method for the optimal control of air flow in a PEMFC with proton exchange membrane fuel cells is presented. The design of the algorithm reflects the principle of marginal effects, in that it can enhance the diversity of samples by continuously switching the form of exploration noise in training to improve the exploration efficiency while preventing Q-value overfitting, improving the robustness of the algorithm and ensuring a better-performing PID controller. When the high-temperature gas from the compressor enters the gas supply line, its temperature and pressure change, so, according to the Law of Energy Conservation and the Ideal Gas Equation, the change of pressure in the gas supply line can be expressed as follows: Fsm,out ksm,out psm − pca (19)
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