Machine Learning Approach for Open Circuit Fault Detection and Localization in EV Motor Drive Systems

Semiconductor devices in electric vehicle (EV) motor drive systems are considered the most fragile components with a high occurrence rate for open circuit fault (OCF). Various signal-based and model-based methods with explicit mathematical models have been previously published for OCF diagnosis. However, this proposed work presents a model-free machine learning (ML) approach for a single-switch OCF detection and localization (DaL) for a two-level, three-phase inverter. Compared to already available ML models with complex feature extraction methods in the literature, a new and simple way to extract OCF feature data with sufficient classification accuracy is proposed. In this regard, the inherent property of active thermal management (ATM) based model predictive control (MPC) to quantify the conduction losses for each semiconductor device in a power converter is integrated with an ML network. This recurrent neural network (RNN)-based ML model as a multiclass classifier localizes the faulty switch based on the dynamics associated with conduction losses as reliable and feature-rich data. The presented approach utilizes the controller data with no additional computational load to compute the feed-in data for the ML model and no extra hardware requirements. The proposed data-driven approach, with an accuracy of 99% for distinct hyperparameters and testing datasets, proves to be a promising solution for OCF DaL.