Remote monitoring system for detection of faults in drive motors of electric vehicles
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Abstract
Electric vehicles (EVs) rely on electric motors (EMs) for drive, offering an eco-friendly alternative to conventional internal combustion engines. However, EMs in EVs are prone to multiple defects, such as bearing faults and load torque fluctuations, induced by electromagnetic interference (EMI), mechanical misalignments, and variable loading conditions arising from dynamic driving environments and controller-induced torque ripple. The resulting external mechanical load on the electric motor, which in turn modulates the stator current, produces distinct fault-related frequency components in the motor stator current spectrum. This study presents a system for remotely monitoring the health of such EMs which are used to drive EVs. A non-invasive fault detection methodology using Motor Current Signature Analysis (MCSA) which has come of age in present day to detect and characterize bearing-related faults and load torque fluctuations is used. The proposed approach is examined and validated on permanent magnet synchronous motors (PMSM), which are predominantly used as drive motors in EVs. A hall effect current sensor in one situation and a current transformer (CT) in another have been used to measure the current waveform of the stator current in the PMSM motors, which is then analyzed using the principles of MCSA. MCSA identifies the fault frequencies associated with bearing defects and torque fluctuations without requiring motor disassembly or additional vibration sensors. By implementing MCSA into a standalone monitoring system, this study demonstrates a reliable means of detecting bearing and load torque-related faults, ultimately improving the durability, efficiency, and operational safety of electric vehicle drivetrains. Future work can explore scaling this approach with cyber-physical system (CPS)-based architectures for real-time monitoring of EVs, enabling centralized analytics and smart decision-making as has been showcased in the present work.
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motor current signature analysis, spectrum, current transformer, load torque
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