Datasheet Driven Switching Loss, Turn-ON/OFF Overvoltage, di/dt, and dv/dt Prediction Method for SiC MOSFET

Cheng, Qian; Wang, Zhi Qiang; Xin, Guoqing; Shi, Xiaojie

doi:10.1109/tpel.2022.3152529

Cited by 24 publications

(6 citation statements)

References 48 publications

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“…The double-pulse test, using inductive loads, is the industry standard for evaluating the performance of SiC MOSFET switches [14,17]. However, the operating environment of a switch in nsPFA-applications differs significantly from that of the double-pulse test.…”

Section: ⅳ Discussionmentioning

confidence: 99%

“…In nsPFA-applications, the inductive component of the biological load is typically negligible, while the resistive component is the dominant factor, on the order of 100 Ω [18]. First, the use of inductive loads leads to desynchronization of Id and Vds, whereas Id and Vds are nearly synchronized for resistive loads [17,19]. Second, with inductive loads, the rise and fall time of the switch is mainly influenced by Cds and Cgd [25].…”

Section: ⅳ Discussionmentioning

confidence: 99%

“…The selection of a SiC MOSFET with suitable performance is essential for nsPG design in nsPFA applications. Nonetheless, most of the key parameters of SiC MOSFETs in the datasheet are obtained using the double-pulse test with inductive loads, which is significantly different from the operating conditions of nsPFA [17]. Specifically, the biological loads primarily exhibit resistive characteristics [18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of SiC MOSFET Key Parameters for Nanosecond Pulsed Field Ablation: Investigating Discrepancies Between Experimental Data and Manufacturer Datasheet Values

Zhu,

Shi,

Guo

et al. 2023

Preprint

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<div>Nanosecond pulsed field ablation (nsPFA) is a promising modality for clinical tissue ablation. The performance of the pulse generator (PG) strongly depends on the switching characteristics of the SiC MOSFET. Currently, many key parameters listed in SiC MOSFET datasheets are determined using double-pulse test circuits with inductive loads, whereas loads in nsPFA-applications are predominantly resistive. This study proposes a test scheme for SiC MOSFETs under nsPFA-like operating conditions. Key parameters to be evaluated are pulsed drain current (IDM), on-resistance (Ron), rise/fall time, turn-on/off delay, and minimum pulse width. The results show significant differences between manufacturer datasheet values and experimental data. For example, one switch under test has an Idm value of 40 A in the datasheet but 153 A in experimental test. This study recommends that custom testing of SiC MOSFETs is essential when designing PGs for nsPFA-applications.</div>

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Section: ⅳ Discussionmentioning

confidence: 99%

Section: ⅳ Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Evaluation of SiC MOSFET Key Parameters for Nanosecond Pulsed Field Ablation: Investigating Discrepancies Between Experimental Data and Manufacturer Datasheet Values

Zhu,

Shi,

Guo

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…To improve the safety of power batteries, most EVs use a battery management system to diagnose battery faults. Many studies have revealed that voltage anomalies are an important cause of battery failure, for example, overvoltage, under-voltage, over-voltage change rate, and poor consistency of cell voltages [7,8]. Therefore, voltage is an essential aspect of battery fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks

et al. 2022

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Multiple faults in new energy vehicle batteries can be diagnosed using voltage. To find voltage fault information in advance and reduce battery safety risk, a state-partitioned voltage fault prognosis method based on the self-attention network is proposed. The voltage data are divided into three parts with typical characteristics according to the charging voltage curve trends under different charge states. Subsequently, a voltage prediction model based on the self-attention network is trained separately with each part of the data. The voltage fault prognosis is realized using the threshold method. The effectiveness of the method is verified using real operating data of electric vehicles (EVs). The effects of different batch sizes and window sizes on model training are analyzed, and the optimized hyperparameters are used to train the voltage prediction model. The average error of predicted voltage is less than 2 mV. Finally, the superiority and robustness of the method are verified.

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“…The model also has constant Cgs characteristics. Temperature dependencies of the switching losses are quite accurately addressed by behavioral models of [16] and [17]. But behavioral nature of these models always raises the question of their predictability with device scaling.…”

Section: Introductionmentioning

confidence: 99%

An Efficient Electro-Thermal Compact Model of SiC Power MOSFETs Including Third Quadrant Behavior

Rashid

Hossain

et al. 2022

IEEE Open J. Power Electron.

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This paper presents an efficient physics-based electrothermal model that solves some advanced problems of modeling Silicon Carbide (SiC) power MOSFETs. It is the first electrothermal model that simulates the temperature dependency of the first and the third quadrant characteristics, including the reverse recovery of the body diode accurately and efficiently. It extends from a previous work that demonstrated the isothermal physicsbased model of the gate-dependent body diode. Physics-based temperature scaling of the first and third quadrant allows simulation of the self-heating effect in a wide range of temperatures (27°C -200°C), even for the synchronous operation. Moreover, a physics-based modeling approach is taken to include gate-voltage dependent non-linearity of the gate to source capacitance (Cgs). Also, a physic-based segmented cascaded method is taken to accurately model the Miller (Crss), and the output (Coss) capacitances at the low and very high drain to source voltage regions. Further, the temperature-dependent breakdown mechanism is included for reliable system design. Double Pulse Tests (DPTs) at various temperatures up to 200°C validate the model's accuracy. Lastly, a synchronous buck converter test demonstrates the model's ability to predict junction temperatures, validating the model's accuracy and efficiency in a continuous operation with self-heating.

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Datasheet Driven Switching Loss, Turn-ON/OFF Overvoltage, di/dt, and dv/dt Prediction Method for SiC MOSFET

Cited by 24 publications

References 48 publications

Evaluation of SiC MOSFET Key Parameters for Nanosecond Pulsed Field Ablation: Investigating Discrepancies Between Experimental Data and Manufacturer Datasheet Values

Evaluation of SiC MOSFET Key Parameters for Nanosecond Pulsed Field Ablation: Investigating Discrepancies Between Experimental Data and Manufacturer Datasheet Values

State-Partial Accurate Voltage Fault Prognosis for Lithium-Ion Batteries Based on Self-Attention Networks

An Efficient Electro-Thermal Compact Model of SiC Power MOSFETs Including Third Quadrant Behavior

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