Real-Time Diagnosis of an In-Wheel Motor of an Electric Vehicle Based on Dynamic Bayesian Networks

This study proposes a new real-time diagnosis method for an in-wheel motor (IWM) of an electric vehicle (EV) based on dynamic Bayesian networks (DBNs). Since the electrical signal of the vehicle power supply is unstable because of the interference resulting from the EV’s frequent acceleration and deceleration, the IWM’s vibration signal is focused. Symptom parameters (SPs) in the time and frequency domains are used to represent different features of the vibration signals in the actual operating conditions of the EV. To select highly sensitive SPs, stable average discrimination rate (SADR) is proposed, which consists of the average discrimination rate (ADR) and the stability coefficient of the group (SCG). Moreover, DBNs are employed to establish a model for the real-time diagnosis of the IWM’s mechanical faults, in which the parameter of road-speed-time slice (RSTS) is used to solve the problem that the state transition probability distribution between two continuous time slices cannot be obtained. Finally, the effectiveness of the proposed methods is verified by experiments using the IWM test bench.

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