Empowering the Performance of Advanced NMPC by Multiparametric Programming—An Application to a PEM Fuel Cell System

Abstract

Fuel cell (FC) systems are part of a prominent key enabling technology for achieving efficient and carbon-free electricity generation and, as such, their optimum operation is of great importance. This work presents the combination of two advanced model predictive control (MPC) methodologies to guarantee the optimal operation of a polymer electrolyte membrane (PEM) fuel cell system. More specifically, at the core of the proposed framework is a nonlinear model predictive control (NMPC) formulation that solves online a nonlinear programming (NLP) problem using a simultaneous direct transcription optimization method. The performance of the NLP solver is enhanced by a warm-start initialization and a search space reduction (SSR) technique. A piecewise affine (PWA) approximation of the variable's feasible space is used to define the boundaries of the search space computed offline, using a multiparametric quadratic programming (mpQP) method. The proposed unified framework is developed and deployed online to an industrial automation system. The response of the multivariable nonlinear controller is assessed through a set of experimental studies, illustrating that the control objectives are achieved and the fuel cell system operates in a stable environment, regardless of the varying operating conditions.

Dynamic Process Operation Under Demand Response – A Review of Methods and Tools

“…A little outside the typical scope of chemical engineering, more recent contributions of NMPC for the operation of systems directly connected to the electric grid can be found. These include large-scale fuel cells and batteries [53,115,126] and of course the huge field of load and frequency control in electric grids [127].…”

Multiparametric Programming in Process Systems Engineering: Recent Developments and Path Forward

“…If none of the precomputed solutions satisfy the optimality conditions, an online step is followed to calculate the optimal solution is found. Ziogou et al (2013) utilized multiparametric programming to speed-up the online solution time of nonlinear MPC. Specifically, an mpQP problem is formulated, based on which tighter bounds for the nonlinear program are constructed, which accelerate the computational performance.…”

Optimum Energy Management of PEM Fuel Cell Systems Based on Model Predictive Control

“…The minimization of functional J (Eq(1)) is subject to constraints of u and y. In this work, two MPC-based strategies are combined in a unified control framework in order to take advantage of their synergistic benefits [7]. The first methodology is an online Nonlinear Model Predictive control (NMPC) strategy, which is very appealing due to its ability to handle dynamic nonlinearities of the process under consideration [6].…”