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Thermal Parameters Identification Method for Induction Machines Based on a Parallel Branches Network Hefei University of Technology
- Format:
- Book
- Conference/Event
- Author/Creator:
- Jiang, Shang, author.
- Conference Name:
- SAE 2024 Vehicle Powertrain Diversification Technology Forum (2024-12-06 : Xi'An, China)
- Language:
- English
- Physical Description:
- 1 online resource cm
- Place of Publication:
- Warrendale, PA SAE International 2025
- Summary:
- Monitoring the rotor temperature of drive machines is crucial for the safety and performance of electric vehicles. However, due to the complex operating conditions of electric vehicles, the thermal parameters of vehicular induction machines (IMs) vary significantly and are difficult to identify accurately. This article first establishes a concise but effective thermal network for IMs and analyzes the influencing factors of thermal parameters. Then, a parameter identification network (PIN) with multiple parallel branches is constructed to learn the mapping relationship between electromechanical variables and thermal parameters. Afterward, temperature datasets for network training are built through bench testing. Finally, the effectiveness of identified parameters for rotor temperature estimation application is verified, demonstrating improved interpretability, generalization ability, and accuracy compared to an end-to-end neural network
- Notes:
- Vendor supplied data
- Publisher Number:
- 2025-01-7054
- Access Restriction:
- Restricted for use by site license
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