A Compact Error Management Algorithm to Minimize False-Alarm Rate of Motor/Generator Faults in (Hybrid) Electric Vehicles

This paper addresses an emerging reliability and safety requirement for more electric transportation systems by proposing an on-board diagnostics plug-in tool. For this purpose, a compact diagnostic algorithm is presented using an advanced speed feedback error management technique for traction motor and generator faults which is one of the most critical parts of the vehicle powertrain. The speed measurement accuracy can be degraded for various reasons such as: sensor issues, vehicle vibration, hardware tolerances, and environmental impacts like temperature, moisture, and electromagnetic interferences. Although the speed feedback errors within an acceptable level can be tolerated by most of the drive systems, the accuracy of speed measurement is critical for the reliability of fault diagnosis. The major reason behind this fact is the frequency of fault signature in the spectrum is primarily determined using the motor speed. It is shown how the reliability of a motor fault diagnosis decision can significantly be improved under erroneous speed feedback conditions by using the developed effective error localization and coarse-to-fine detection technique. The proposed scheme is implemented through effective strategies to minimize computational complexity and memory occupancy in every step of signal processing according to manufacturers' strict cost limitation.