|
|
Fault tolerant control of distributed hub motor driven vehicle based on temperature expiration condition |
1. School of Automotive Engineering, Wuhan University of Technology, Wuhan, Hubei 430070, China; 2. Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan,Hubei 430070, China; 3. Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology, Wuhan, Hubei 430070, China; 4. Hubei Engineering Technology Research Center for New Energy and Intelligent Connected Vehicle, Wuhan University of Technology, Wuhan, Hubei 430070, China |
|
|
Abstract Wheel torques of vehicles driven by hub motors are independently controllable with rapid dynamic response and redundant configuration of drive system, which have significant advantages in integrated vehicle dynamics control. However, the complex driving conditions and high power demand are likely to cause temperature-rise fault of the motor, which can ultimately affect driving efficiency. To investigate the temperature fault diagnosis of hub motor and the torque distribution strategy of each wheel under the temperature expiration condition, and ensure the stability and power performance of the vehicle, the fuzzy logic detection algorithm was designed to diagnose the temperature fault of the motor, and the temperature failure factor was proposed to quantify the failure state of the hub motor. The longitudinal and lateral force requirements were calculated according to the expected speed and front wheel angle, and the fault tolerant control strategy was designed to achieve the optimal distribution of torque reconstruction. The proposed algorithm was verified on a prototype vehicle. The results show that the fault diagnosis module and the solution strategy can judge the motor failure state in time and reconstruct the torque distribution, which improves the driving safety and achieves the coordinated control of the vehicle dynamics and stability.
|
Received: 15 October 2021
|
|
|
|
[1] |
SRIDHAR C, DEVI N R. Fault diagnosis of three-phase electrically-excited synchronous motor by using adaptive threshold algorithm[J]. Materials Today: Proceedings,DOI: 10.1016/j.matpr.2020.10.844.
|
[2] |
ZHANG D, LIU G H, ZHOU H W, et al. Adaptive sliding mode fault-tolerant coordination control for four-wheel independently driven electric vehicles[J]. IEEE Transactions on Industrial Electronics, 2018, 65(11): 9090-9100.
|
[3] |
GUO B, CHEN Y. Robust adaptive fault-tolerant control of four-wheel independently actuated electric vehicles[J]. IEEE Transactions on Industrial Informatics,2018,16(5):2882-2894.
|
[4] |
WANG R R, WANG J M. Passive actuator fault-tolerant control for a class of overactuated nonlinear systems and applications to electric vehicles[J]. IEEE Transactions on Vehicular Technology, 2013, 62(3):972-985.
|
[5] |
WANG Y L, YU S Y, YUAN J X, et al. Fault-tolerant control of electric ground vehicles using a triple-step nonlinear approach[J]. IEEE/ASME Transactions on Mechatronics, 2018,23(4):1775-1786.
|
[6] |
ZHANG G G, ZHANG H, HUANG X Y, et al. Active fault-tolerant control for electric vehicles with indepen-dently driven rear in-wheel motors against certain actuator faults[J]. IEEE Transactions on Control Systems Technology, 2016,24(5):1557-1572.
|
[7] |
夏万林. 智能分布式驱动电动车动力学控制方法研究[D]. 重庆:重庆理工大学, 2018.
|
[8] |
GUO L, GE P S, SUN D C. Torque distribution algorithm for stability control of electric vehicle driven by four in-wheel motors under emergency conditions[J]. IEEE Access, 2019,7:104737-104748.
|
[9] |
张云灵. 基于模型预测的分布式驱动电动汽车稳定性控制[D]. 长沙:湖南大学, 2018.
|
[10] |
王震坡,丁晓林,张雷.四轮轮毂电机驱动电动汽车驱动防滑控制关键技术综述[J].机械工程学报,2019,55(12):99-120.
|
|
WANG Z P, DING X L, ZHANG L. Overview on key technologies of acceleration slip regulation for four-wheel-independently-actuated electric vehicle[J].Journal of Mechanical Engineering, 2019,55(12):99-120. (in Chinese)
|
[11] |
梁艺潇,李以农,余颖弘,等.基于神经网络逆系统的智能汽车纵横向解耦控制[J].湖南大学学报(自然科学版),2019,46(10):26-35.
|
|
LIANG Y X, LI Y N, YU Y H, et al. Decoupling control of longitudinal and lateral motion for intelligent vehicle based on neural network inverse method[J]. Journal of Hunan University(Natural Sciences), 2019,46(10):26-35. (in Chinese)
|
|
|
|