Nonlinear predictive control for durability enhancement and efficiency improvement in a fuel cell power system

JOURNAL OF POWER SOURCES

卷:328

Article

Luna, Julio¹  Jemei, Samir^2,3  Yousfi-Steiner, Nadia^2,3,4  Husar, Attila¹  Serra, Maria¹  Hissel, Daniel^2,3 

[1] CSIC UPC, Inst Robot & Informat Ind, C Llorens i Artigas 4-6, Barcelona 08028, Spain

[2] Univ Bourgogne Franche Comte, FEMTO ST UMR CNRS 6174, Besancon, France

[3] FR CNRS 3539, FCLAB, Belfort, France

[4] LABEX ACT CNRS, Besancon, France

关键词: Proton exchange membrane fuel cell;    Distributed parameter system;    Nonlinear observer;    Model predictive control;    Degradation;    Starvation;   

DOI  :  10.1016/j.jpowsour.2016.08.019

来源: Elsevier

【 摘 要 】

In this work, a nonlinear model predictive control (NMPC) strategy is proposed to improve the efficiency and enhance the durability of a proton exchange membrane fuel cell (PEMFC) power system. The PEMFC controller is based on a distributed parameters model that describes the nonlinear dynamics of the system, considering spatial variations along the gas channels. Parasitic power from different system auxiliaries is considered, including the main parasitic losses which are those of the compressor. A nonlinear observer is implemented, based on the discretised model of the PEMFC, to estimate the internal states. This information is included in the cost function of the controller to enhance the durability of the system by means of avoiding local starvation and inappropriate water vapour concentrations. Simulation results are presented to show the performance of the proposed controller over a given case study in an automotive application (New European Driving Cycle). With the aim of representing the most relevant phenomena that affects the PEMFC voltage, the simulation model includes a two-phase water model and the effects of liquid water on the catalyst active area. The control model is a simplified version that does not consider two-phase water dynamics. (C) 2016 Elsevier B.V. All rights reserved.

【 授权许可】

Free