Nonlinear observation of internal states of fuel cell cathode utilizing a high-order sliding-mode algorithm

被引:25
|
作者
Xu, Liangfei [1 ,2 ,3 ]
Hu, Junming [1 ,3 ]
Cheng, Siliang [1 ,3 ]
Fang, Chuan [1 ,3 ]
Li, Jianqiu [1 ,3 ]
Ouyang, Minggao [1 ]
Lehnert, Werner [2 ,4 ]
机构
[1] Tsinghua Univ, Dept Automot Engn, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China
[2] Forschungszentrum Julich, IEK Electrochem Proc Engn 3, Inst Energy & Climate Res, D-52425 Julich, Germany
[3] Collaborat Innovat Ctr Elect Vehicles Beijing, Beijing 100081, Peoples R China
[4] Rhein Westfal TH Aachen, Modeling Electrochem Proc Engn, D-52062 Aachen, Germany
基金
中国国家自然科学基金;
关键词
Polymer electrolyte membrane fuel cell; Dynamic model; Second-order sliding mode; Observer; Partial pressure; Air stoichiometry; PREDICTIVE CONTROL; ELECTRIC VEHICLES; PEMFC MODEL; SYSTEM; OBSERVER; EXCHANGE; DESIGN; STRATEGY; FLOW; DEGRADATION;
D O I
10.1016/j.jpowsour.2017.04.068
中图分类号
O64 [物理化学(理论化学)、化学物理学];
学科分类号
070304 ; 081704 ;
摘要
A scheme for designing a second-order sliding-mode (SOSM) observer that estimates critical internal states on the cathode side of a polymer electrolyte membrane (PEM) fuel cell system is presented. A nonlinear, isothermal dynamic model for the cathode side and a membrane electrolyte assembly are first described. A nonlinear observer topology based on an SOSM algorithm is then introduced, and equations for the SOSM observer deduced. Online calculation of the inverse matrix produces numerical errors, so a modified matrix is introduced to eliminate the negative effects of these on the observer. The simulation results indicate that the SOSM observer performs well for the gas partial pressures and air stoichiometry. The estimation results follow the simulated values in the model with relative errors within +/- 2% at stable status. Large errors occur during the fast dynamic processes (<1 s). Moreover, the nonlinear observer shows good robustness against variations in the initial values of the internal states, but less robustness against variations in system parameters. The partial pressures are more sensitive than the air stoichiometry to system parameters. Finally, the order of effects of parameter uncertainties on the estimation results is outlined and analyzed. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码:56 / 71
页数:16
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