Neural network based model predictive control of voltage for a polymer electrolyte fuel cell system with constraints

TL;DR

This paper presents a neural network-based model predictive control method to regulate the voltage of a polymer electrolyte fuel cell system while ensuring safety constraints on hydrogen pressure and input rates.

Contribution

It introduces a neural network model predictive control approach that stabilizes fuel cell voltage and manages safety constraints, matching the performance of traditional physical model-based MPC.

Findings

Successfully stabilizes voltage under workload disturbances

Satisfies safety constraints on hydrogen pressure and input change rates

Performs comparably to physical model-based MPC

Abstract

A fuel cell system must output a steady voltage as a power source in practical use. A neural network (NN) based model predictive control (MPC) approach is developed in this work to regulate the fuel cell output voltage with safety constraints. The developed NN MPC controller stabilizes the polymer electrolyte fuel cell system's output voltage by controlling the hydrogen and air flow rates at the same time. The safety constraints regarding the hydrogen pressure limit and input change rate limit are considered. The neural network model is built to describe the system voltage and hydrogen pressure behavior. Simulation results show that the NN MPC can control the voltage at the desired value while satisfying the safety constraints under workload disturbance. The NN MPC shows a comparable performance of the MPC based on the detailed underlying system physical model.