A Sensor Fault Diagnosis Scheme for a DC/DC Converter used in Hybrid Electric Vehicles

Abstract

With the extensive use of power converters in modern hybrid vehicles, rises the need for efficient methods of condition monitoring and fault diagnosis to ensure the reliability of the automotive electrical power system. This paper addresses detection and isolation of sensor faults in a DC/DC power converter system interfacing the main energy storage unit and the AC drive in a hybrid electric vehicle. A residual-based fault diagnosis scheme that timely detects and localizes sensor faults in the examined system is designed. Residual signals are derived from comparison between estimates generated by a bank of extended Kalman filters and real measurements obtained from a hardware prototype of the power converter. The generalized likelihood ratio test is utilized in a statistical hypothesis testing framework to evaluate the residuals and detect the occurrence of sensor faults. The receiver operating characteristic curve is then employed to optimize the value of the detection threshold and the sliding window width of the statistical test. The proposed algorithm is tested on data obtained from simulated and real measurements with faults injected on the converter sensors. The results obtained promise maximum probability of correct detection and minimum false alarm rate.