汽车电子研究中心
Research / Vehicle Control System
Research Direction

Based on the development of the intelligent driving vehicles and the related key technologies, systematic interdisciplinary research has been carried out from the communications security, laser radar, machine vision, active safety, energy saving such aspects.
1.Perception
Research on the information fusion technology of the vehicle radar and machine vision and new technology of space 3D information construction in visual detection and measurement technology
2.Autonomous driving decision
Based on the framework of deep reinforcement learning, to construct the automatic driving strategy learning model and to improve the training efficiency using parallel computing.
3.Car network communication security
Research on the encryption and decryption, identity authentication and key distribution of vehicle network access, safe driving, billing management, traffic management and data communication.
4.Energy-saving driving
Through the path planning and speed adjustment to achieve energy-saving, combined with large traffic data, so as to achieve energy conservation from the city level.

High accuracy optimal efficiency control of IPMSM drive

In order to optimally control the interior permanent magnet synchronous machine (IPMSM) drives, maximum torque per ampere (MTPA) control has been widely applied to various applications, e.g., electric vehicle, high-speed rail, etc. After around 30 years’ development, the MTPA control schemes which calculate the MTPA points online based on the machine parameters became one kind of the most important and well-adopted MTPA control methods. However, according to the latest researches, since the most of these MTPA control schemes ignore the terms of the partial differentiation of machine parameters with respect to current angle, therefore, even substitutes accurate machine parameters into these MTPA control schemes, these traditional online MTPA point calculation schemes will still result in large MTPA control errors and there is no research on the observation of these differentiation terms has been reported yet. Based on the above findings, this project will innovatively develop novel observers to observe the partial differentiation of machine parameters with respect to current angle terms online and comprehensively study the effects of these differentiation terms on MTPA point calculation. Further, the project will develop robust MTPA control schemes with high MTPA control accuracy but insensitive to machine parameter errors and the developed MTPA control schemes will also be extended to flux weakening region.

Intellectual Properties

Selected Papers

1. T. Sun, J. Wang, M. Koc, “Self-learning Direct Flux Vector Control of Interior Permanent Magnet Machine Drives”, IEEE Transactions on Power Electronics., vol. 32, no. 6, pp. 4652 - 4662, 2017.

2. T. Sun, J. Wang, M. Koc, “On Accuracy of Virtual Signal Injection based MTPA Operation of Interior Permanent Magnet Synchronous Machine Drives”, IEEE Transactions on Power Electronics., vol. 32, no. 9, pp. 7405 - 7408, 2017.

3. T. Sun, J. Wang, M. Koc, X. Chen, “Self-Learning MTPA Control of Interior Permanent Magnet Synchronous Machine Drives Based on Virtual Signal Injection”, IEEE Transactions on Industrial Applications., vol. 52, no. 4, pp. 3062– 3070, 2016.

4. T. Sun, J. Wang, M. Koc, “Virtual Signal Injection Based Direct Flux Vector Control of IPMSM Drives”, IEEE Transactions on Industrial Electronics., vol. 63, no. 8, pp. 4773–4782, 2016.

5. T. Sun, J. Wang, “ Extension of Virtual Signal Injection Based MTPA Control for Interior Permanent Magnet Synchronous Machine Drives into Field Weakening Region”, IEEE Transactions on Industrial Electronics., vol. 62, no. 11, pp. 6809–6817, 2015.

6. T. Sun, J. Wang, X. Chen,“ Maximum Torque per Ampere (MTPA) Control for Interior Permanent Magnet Synchronous Machine Drives Based on Virtual Signal Injection”, IEEE Transactions on Power Electronics., vol. 30, no. 9, pp. 5036–5045, 2015.


Selected Patents