Intelligent traction inverter in next generation electric vehicles: The health monitoring of silicon-carbide power modules
Articolo
Data di Pubblicazione:
2023
Citazione:
Intelligent traction inverter in next generation electric vehicles: The health monitoring of silicon-carbide power modules / C. Pino, A. Sitta, G. Castagnolo, A.A. Messina, S. Coffa, M. Calabretta, F. Scotti, A. Genovese, V. Piuri, C. Spampinato, F. Rundo. - In: IEEE TRANSACTIONS ON INTELLIGENT VEHICLES. - ISSN 2379-8904. - 8:(2023), pp. 12.4734-12.4753. [10.1109/TIV.2023.3294726]
Abstract:
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 VdsLS 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 VdsLS signal, significantly more performing than the existing state-of-the-art architectures. The proposed deep pipeline has been tested on several ACEPACK TM DRIVE SiC power modules delivered by STMicroelectronics, with an average error of about 0.2% , confirming the effectiveness of the proposed system.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
Temporal Fusion Transformer; SiC Power Module; Device Health Monitoring;
Elenco autori:
C. Pino, A. Sitta, G. Castagnolo, A.A. Messina, S. Coffa, M. Calabretta, F. Scotti, A. Genovese, V. Piuri, C. Spampinato, F. Rundo
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