Particle filter based hybrid prognostics of proton exchange membrane fuel cell in bond graph framework

被引：62

作者：

Jha, Mayank Shekhar ^{[1
]}

Bressel, Mathieu ^{[2
,3
]}

Ould-Bouamama, Belkacem ^{[2
]}

Dauphin-Tanguy, Genevieve ^{[1
]}

机构：

[1] Ecole Cent Lille, CRIStAL, UMR 9189, CNRS, Cite Sci, F-59650 Villeneuve Dascq, France

[2] Univ Lille 1, Polytech Lille, CRIStAL, UMR 9189,CNRS, Cite Sci, F-59650 Villeneuve Dascq, France

[3] CNRS, FR 3539, FCLAB, FEMTO ST,UMR 6174, Rue Thierry Mieg, F-90000 Belfort, France

来源：

COMPUTERS & CHEMICAL ENGINEERING | 2016年 / 95卷

关键词：

Prognostics; Bond graph; Particle filters; PEM fuel cell; Remaining useful life; REMAINING USEFUL LIFE; DURABILITY; MODEL; DEGRADATION; SIMULATION; PREDICTION; DIAGNOSIS; SYSTEMS;

D O I：

10.1016/j.compchemeng.2016.08.018

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper presents a holistic solution towards prognostics of industrial Proton Exchange Membrane Fuel Cell. It involves an efficient multi-energetic model suited for diagnostics and prognostics, developed using some specific properties of Bond Graph (BG) theory. The benefits of Particle Filters (PF) are integrated with the BG model derived fault indicators named Analytical Redundancy Relations, for prognostics of the Electrical-Electrochemical part. The hybrid prognostics involves statistical degradation model obtained using real degradation tests. Prognostics problem is formulated as the joint state-parameter estimation problem in PF framework where estimations of state of health (SOH) is obtained in probabilistic domain. This in turn is used for prediction of Remaining Useful Life (RUL) under constant current as well as dynamic current solicitations. The SOH estimation and RUL prediction is obtained with very high accuracy and precise confidence bounds. Moreover, a comparative analysis with Extended Kalman Filter demonstrates the usefulness of PF. (C) 2016 Elsevier Ltd. All rights reserved.

引用

页码：216 / 230

页数：15