Volume 5: Innovative Solutions for Energy Transitions: Part IV

Deep Learning Based Hierarchical Predictive Control for Oxygen Stoichiometry of Proton Exchange Membrane Fuel Cell Engine Xuechao Wang, Jinzhou Chen, Shengwei Quan, Hongwen He, Ya-Xiong Wang

Abstract

A deep learning based hierarchical predictive control is developed for regulating the oxygen stoichiometry of proton exchange membrane fuel cell (PEMFC) engine in this study. Firstly, a hierarchical predictive control scheme is proposed by designing the first-level predictor to determine the operation current of PEMFC engine, and then the second-level model predictive control (MPC) generating robust control input. BP neural network is selected to formulate the first-level prediction model and airflow model is linearized to design MPC with suitable prediction horizon and control horizon. A simulation test is carried out through operating in a mixed driving cycle MANHATTAN + (a part of) UDDS to verify the efficacy of the proposed method. The results indicate that the oxygen stoichiometry tracks the reference value well avoiding the starvation of the PEMFC engine.

Keywords Proton exchange membrane fuel cell (PEMFC), oxygen stoichiometry, hierarchical control, deep BP neural network, model predictive control