PROJECT TITLE :

A Combinational-Logic Method for Electric Vehicle Drivetrain Fault Diagnosis

ABSTRACT:

This paper presents a combinational-logic-primarily based approach for identifying faults that would occur in the drivetrain of an electric vehicle (EV). A real-time simulation model of an EV is used to study the behavior of accessible sensor signals and management commands, with specific measured quantities, throughout vehicle operation. Those quantities may be, however are not limited to, mean values of the part currents, DC bus current, traction motor speed (if out there), additionally to control command quantities. Focus is given to those quantities that are abundant in vehicle inverters and controllers for no added sensing prices. Those quantities carry information of offsets and disturbances that could occur below faulty operating conditions compared to nominal operation, and are thus observed and studied beneath these conditions. Using such info, a technique is developed using easy combinational logic and thresholds to diagnose a fault occurring at any time during a vehicle drive cycle. By combining options of measured quantities that behave equally irrespective of when the fault happens throughout a driving cycle, the proposed method is fault-time-insensitive. The proposed method is presented and validated by real-time simulations to capture over 20 different faults injected at completely different drive cycle times and in different drivetrain parts-electrical machine, inverter, transmission, and sensors. Results show that the proposed method is able to robustly and successfully diagnose different faults no matter when would they occur.