Safe and Efficient Polymer Electrolyte Membrane Fuel Cell Control Using Successive Linearization Based Model Predictive Control Validated on Real Vehicle Data

Author:

Vrlić Martin,Ritzberger Daniel,Jakubek Stefan

Abstract

In this paper, a polymer electrolyte membrane fuel cell (PEMFC) stack control study is presented. The goal is to track the transient power demand of a real fuel cell (FC) vehicle while ensuring safe and efficient operation. Due to the dynamically changing power demand, fast transients occur in the internal states of the fuel cell (e.g., pressure, humidity, reactant mass) leading to degradation effects (e.g., high/low membrane overpressure, reactants starvation) which are avoided by imposing safety constraints. Efficiency is considered in terms of internal voltage losses minimization as well as minimization of the power of the compressor used to pressurize the cathode. For solving the optimization problem of power demand tracking, adhering to safety constraints, and maximizing efficiency, model predictive control (MPC) has been chosen. Due to the nonlinearity of the FC system, a successive linearization based MPC (SLMPC) is used to control the FC throughout its operating region. Simulation results show that the power demand can be fulfilled while at the same time ensuring safe operation in terms of adhering to constraints and that the minimization of internal voltage losses and compressor power lead to an approximate 9.5% less hydrogen consumption than in the actual reference vehicle.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous)

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2. Data-Driven Predictive Control With Online Adaption: Application to a Fuel Cell System;IEEE Transactions on Control Systems Technology;2024-01

3. Nonlinear MPC for Fuel Cell Air Path Control with Experimental Validation;2023 31st Mediterranean Conference on Control and Automation (MED);2023-06-26

4. Numerical Integration for Nonlinear Model Predictive Control of a Fuel Cell System;2023 American Control Conference (ACC);2023-05-31

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