Digital Signal Processing

Smart EV Motor Predictive Maintenance System Using IoT

Authors: Tanjim Pirjade, Kishor Edage, Shivraj Yewale, Shreyansh Chougule
Comments: 13 Pages. (Note by viXra Admin: Please submit article written with AI assistance to ai.viXra.org)

Electric motors sit at the heart of an electric vehicle (EV); in fact, what's more, breakdowns, safety risks and costly repairs are being caused by a sudden motor failure, on the whole. Well the same, after a fault has already start; motor condition is still mostly verified at fixed service intervals. Low-cost sensors,microcontrollers with built-in Wi-Fi and cloud dashboards now make continuous condition monitoring practical; this review is evaluating motor fault mechanisms, condition-monitoring methods, sensorbased measurement of temperature, vibration, current and speed, Internet of Things (IoT) architectures, predictive maintenance and health-index methods, and examines recurring obstacles, like noisy signals, false alarms, scarce fault data, changing load conditions and the restricted computing power of embedded nodes, broadly speaking. The survey is, for all practical purposes, suggesting that mostpublished work tackles one fault indicator at a time or rests on costly instrumentation. When you want to finish this gap, a Smart EV Motor Predictive Maintenance System is put forward that combines temperature, vibration, current and speed sensing on an ESP32 microcontroller, finds a motor health score, raises early maintenance warnings and offers all data on a real-time IoT dashboard; this model is at the design stage, such as no measured results are reported. Try this instead, we state the targets, the planned methodology, an evaluation protocol and the outcomes we expect, clearly marked as predictions to be tested, and argue that such a low-cost platform can shift motor maintenance from reactive repair to proactive, transparent and doable decisions.
Category: Digital Signal Processing