Intelligent Traction Inverter in Next Generation Electric Vehicles: The Health Monitoring of Silicon-Carbide Power Modules

In automotive and industrial domains, the 'health monitoring' or 'condition monitoring' of electronic devices is gradually playing a key role in manufacturing processes and innovation roadmaps. The concept of health monitoring is often related to the so-called 'residual lifetime' of the monitored system. In this work, the authors have designed a deep learning system for the health monitoring of power devices in Silicon Carbide (SiC) technology used in the Traction Inverter Systems of the latest generation electric cars. A Temporal Fusion Transformer embedding such layers of Temporal Convolutional Network with a Multi-Head Attention block for the robust lifetime assessment of SiC power devices, is proposed. Specifically, the designed system predicts such future samples of the ON-state voltage between drain and source of the low-side part of the SiC power module VdsLS, in half-bridge configuration. Extensive literature confirmed that the Vds LS signal can be efficiently used as a robust predictive device-degradation marker. Through the learning of the temporal feature relationships at different scales and the intelligent selection of relevant input features, the proposed solution will discard unnecessary input dynamics building a multi-step predictive model of the Vds LS signal, significantly more performing than the existing state-of-the-art architectures. The proposed deep pipeline has been tested on several ACEPACK DRIVE SiC power modules delivered by STMicroelectronics, with an average error of about 0.2%, confirming the effectiveness of the proposed system.