Abstract
This paper presents an improved direct torque control (DTC) algorithm using an artificial neural network (ANN) for open-end winding induction motor (OEWIM) drives. A look-up table (LUT) selects the voltage vectors in conventional direct torque control (CDTC). The dwell time of these vectors depends on the hysteresis bands. The ANN gives the flexibility of dividing this band into smaller levels. In this paper, ANN is used to improve the performance of DTC. An attempt is made to apply ANN-based DTC to an electric vehicle (EV) powertrain with an OEWIM configuration. The performance of the powertrain is validated through simulations and a laboratory experimental setup, and comparative results are presented.
| Original language | British English |
|---|---|
| Title of host publication | 2024 IEEE Transportation Electrification Conference and Expo, ITEC 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350317664 |
| DOIs | |
| State | Published - 2024 |
| Event | 2024 IEEE Transportation Electrification Conference and Expo, ITEC 2024 - Chicago, United States Duration: 19 Jun 2024 → 21 Jun 2024 |
Publication series
| Name | 2024 IEEE Transportation Electrification Conference and Expo, ITEC 2024 |
|---|
Conference
| Conference | 2024 IEEE Transportation Electrification Conference and Expo, ITEC 2024 |
|---|---|
| Country/Territory | United States |
| City | Chicago |
| Period | 19/06/24 → 21/06/24 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Artificial neural network
- direct torque control
- electric vehicle
- field oriented control
- open-end winding induction motor
- torque ripple
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