Robust Monitoring of an Electric Vehicle With Structured and Unstructured Uncertainties

被引：31

作者：

Djeziri, Mohand Arab ^{[1
]}

Merzouki, Rochdi ^{[1
]}

Bouamama, Belkacem Ould ^{[1
]}

机构：

[1] Univ Lille, Ecole Polytech, CNRS, Lab Automat Genie Informat & Signal,UMR 8146, F-59651 Villeneuve Dascq, France

来源：

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY | 2009年 / 58卷 / 09期

关键词：

Analytical redundancy relations (ARRs); bond graph (BG); electric vehicle; fault detection and isolation (FDI); linear fractional transformations (LFTs); structured and unstructured uncertainties; FAULT-DIAGNOSIS;

D O I：

10.1109/TVT.2009.2026281

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper deals with a robust fault-detection and isolation (FDI) technique, which is applied to the traction system of an electric vehicle, in the presence of structured and unstructured uncertainties. Due to the structural and multidomain properties of the bond graph, the generation of a nonlinear model and residuals for the studied system with adaptive thresholds is synthesized. The parameters and structured uncertainties are identified by using a least-square algorithm. A super-twisting observer is used to estimate both unstructured uncertainties and unknown inputs. Cosimulation with real experimental data shows the robustness of the residuals to the considered uncertainties and their sensitivity to the faults.

引用

页码：4710 / 4719

页数：10

共 20 条

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