Real-Time On-MCU Open-Circuit Fault Diagnosis of Electric-Vehicle Inverters Using a Lightweight Angular Sector-Energy Network

Abstract

Power-switch open-circuit (OC) faults distort electric-vehicle (EV) inverter phase currents and require fast on-board diagnosis for fault-tolerant control. Trajectory-image methods encode the α–β current-vector trajectory as a binary image and classify it with a convolutional neural network (CNN); however, the baseline uses 6.46×105 parameters and 3.31×107 multiply–accumulate (MAC) operations per inference, which is costly for motor-control microcontrollers (MCUs). Here, each one-cycle trajectory is represented by a 36-dimensional normalized angular sector-energy vector and classified by a compact two-stage multilayer perceptron. Sector accumulation averages zero-mean measurement noise in the representation, without relying on noise-augmented training. The locating stage uses 1.58×104 parameters and 1.56×104 MACs per inference, 97.55% and 99.95% fewer than the baseline CNN; the complete pipeline runs on a TI F28379D in 0.52 ms. On measured resistive-load currents, both methods reach 100% accuracy from 40 to 20 dB, whereas the proposed method remains more accurate at 15 and 10 dB, including under 88% phase-current unbalance. A supplementary balanced RL-load experiment preserves 100% clean accuracy, confirming MCU-executable diagnosis under a lagging power-factor load for embedded EV inverter protection.