Analytical models of the crosstalk voltage in SiC MOSFETs under different loads

Xu, Hong‐Ji; Cai, Yumeng; Sun, Peng; Zhao, Zhibin; Cao, Boyuan

doi:10.1049/pel2.12554

Cited by 1 publication

(1 citation statement)

References 19 publications

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“…The monitoring system observes the inverter indirectly through the controlled electric machine. In literature, a key aging indicator for power devices at high switching frequencies is the variation in R ds (on) , causing a noticeable reduction in current (I d (on) ) compared to its intrinsic behavior, often characterized by temperature-dependent traits [77][78][79][80][81]. In the realistic model, the output current of the machine Park model is adjusted by a scaling factor tied to the aging parameter.…”

Section: Sic Mosfet Ageing Modelingmentioning

confidence: 99%

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

Dini,

Basso,

Saponara

et al. 2024

IET Power Electronics

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The article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

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