Online implementation of SVM based fault diagnosis strategy for PEMFC systems

被引：99

作者：

Li, Zhongliang ^{[1
,2
]}

Outbib, Rachid ^{[3
]}

Giurgea, Stefan ^{[1
,2
]}

Hissel, Daniel ^{[1
,2
]}

Jemei, Samir ^{[1
,2
]}

Giraud, Alain ^{[4
]}

Rosini, Sebastien ^{[5
]}

机构：

[1] CNRS, FR 3539, FCLAB Fuel Cell Lab Res Federat, Rue Thierry Mieg, F-90010 Belfort, France

[2] UFC UTBM ENSMM, Dept Energy, CNRS, FEMTO ST,UMR 6174, Paris, France

[3] Univ Aix Marseille, LSIS, Marseille, France

[4] CEA, LIST, F-91191 Gif Sur Yvette, France

[5] CEA Grenoble, LITEN, F-38054 Grenoble, France

来源：

APPLIED ENERGY | 2016年 / 164卷

关键词：

PEMFC system; Fault diagnosis; SVM classification; System in Package; Online implementation; FUEL-CELL; MODEL; STACK; METHODOLOGIES;

D O I：

10.1016/j.apenergy.2015.11.060

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

In this paper, the topic of online diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC) systems is addressed. In the diagnosis approach, individual cell voltages are used as the variables for diagnosis. The pattern classification tool Support Vector Machine (SVM) combined with designed diagnosis rule is used to achieve fault detection and isolation (FDI). A highly-compacted embedded system of the System in Package (SiP) type is designed and fabricated to monitor individual cell voltages and to perform the diagnosis algorithms. For validation, the diagnosis approach is implemented online on PEMFC experimental platform. Four concerned faults can be detected and isolated in real-time. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：284 / 293

页数：10

共 41 条

[11]

Giraud A., 2012, EMBEDDED SMART SENSO

[12] Adaptive extended kalman filter based fault detection and isolation for a lithium-ion battery pack [J].

He, Hongwen ;

Liu, Zhentong ;

Hua, Yin .

CLEAN, EFFICIENT AND AFFORDABLE ENERGY FOR A SUSTAINABLE FUTURE, 2015, 75 :1950-1955

[13] A comparison of methods for multiclass support vector machines [J].

Hsu, CW ;

Lin, CJ .

IEEE TRANSACTIONS ON NEURAL NETWORKS, 2002, 13 (02) :415-425

[14] Proton exchange membrane fuel cell system diagnosis based on the multivariate statistical method [J].

Hua, Jianfeng ;

Li, Jianqiu ;

Ouyang, Minggao ;

Lu, Languang ;

Xu, Liangfei .

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2011, 36 (16) :9896-9905

[15] Dynamic cell performance of kW-grade proton exchange membrane fuel cell stack with dead-ended anode [J].

Jang, Jer-Huan ;

Yan, Wei-Mon ;

Chiu, Han-Chieh ;

Lui, Jun-Yi .

APPLIED ENERGY, 2015, 142 :108-114

[16] Prognostics and Health Management of PEMFC - State of the art and remaining challenges [J].

Jouin, Marine ;

Gouriveau, Rafael ;

Hissel, Daniel ;

Pera, Marie-Cecile ;

Zerhouni, Noureddine .

INTERNATIONAL JOURNAL OF HYDROGEN ENERGY, 2013, 38 (35) :15307-15317

[17] Dynamic simulation of a fuel cell hybrid vehicle during the federal test procedure-75 driving cycle [J].

Kang, Sanggyu ;

Min, Kyoungdoug .

APPLIED ENERGY, 2016, 161 :181-196

[18] C-terminal de novo sequencing of peptides using oxazolone-based derivatization with bromine signature [J].

Kim, Jong-Seo ;

Shin, Mansup ;

Song, Jin-Su ;

An, Songhie ;

Kim, Hie-Joon .

ANALYTICAL BIOCHEMISTRY, 2011, 419 (02) :211-216

[19]

Li Z., 2014, THESIS AIX MARSEILLE

[20] Diagnosis for PEMFC Systems: A Data-Driven Approach With the Capabilities of Online Adaptation and Novel Fault Detection [J].

Li, Zhongliang ;

Outbib, Rachid ;

Giurgea, Stefan ;

Hissel, Daniel .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2015, 62 (08) :5164-5174

← 1 2 3 4 5 →