Details
| Original language | English |
|---|---|
| Title of host publication | 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Number of pages | 10 |
| ISBN (electronic) | 9789075815399 |
| ISBN (print) | 978-1-6654-8700-9 |
| Publication status | Published - 2022 |
| Event | 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe - Hanover, Germany Duration: 5 Sept 2022 → 9 Sept 2022 |
Abstract
Electric vehicle drivetrains using wide-bandgap semiconductors face challenges regarding EMI and ac-celerated machine aging due to the fast switching transients. This paper presents a method of controlling a variable-resistance active gate driver with the help of a neural network in order to reduce the drawbacks of fast switching while increasing efficiency. Measurements covering the whole MOSFET operating range and a sinusoidal inverter output current prove that the proposed method effectively reduces losses while also reducing switching speed and, in this way, reduces EMI issues and machine damage.
Keywords
- EMC/EMI, Neural network, Power converters for EV, Silicon Carbide (SiC), Smart Gate Drivers
ASJC Scopus subject areas
- Energy(all)
- Energy Engineering and Power Technology
- Engineering(all)
- Electrical and Electronic Engineering
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24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe. Institute of Electrical and Electronics Engineers Inc., 2022.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Control of an Active Gate Driver for an Electric Vehicle Traction Inverter Using Artificial Neural Networks
AU - Wiesemann, Julius
AU - Dumtzlaff, Jacob
AU - Mertens, Axel
PY - 2022
Y1 - 2022
N2 - Electric vehicle drivetrains using wide-bandgap semiconductors face challenges regarding EMI and ac-celerated machine aging due to the fast switching transients. This paper presents a method of controlling a variable-resistance active gate driver with the help of a neural network in order to reduce the drawbacks of fast switching while increasing efficiency. Measurements covering the whole MOSFET operating range and a sinusoidal inverter output current prove that the proposed method effectively reduces losses while also reducing switching speed and, in this way, reduces EMI issues and machine damage.
AB - Electric vehicle drivetrains using wide-bandgap semiconductors face challenges regarding EMI and ac-celerated machine aging due to the fast switching transients. This paper presents a method of controlling a variable-resistance active gate driver with the help of a neural network in order to reduce the drawbacks of fast switching while increasing efficiency. Measurements covering the whole MOSFET operating range and a sinusoidal inverter output current prove that the proposed method effectively reduces losses while also reducing switching speed and, in this way, reduces EMI issues and machine damage.
KW - EMC/EMI
KW - Neural network
KW - Power converters for EV
KW - Silicon Carbide (SiC)
KW - Smart Gate Drivers
UR - http://www.scopus.com/inward/record.url?scp=85141559407&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85141559407
SN - 978-1-6654-8700-9
BT - 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 24th European Conference on Power Electronics and Applications, EPE 2022 ECCE Europe
Y2 - 5 September 2022 through 9 September 2022
ER -