Robust Monitoring of an Electric Vehicle With Structured and Unstructured Uncertainties

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.

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