A Novel Driver Model for Real-time Simulation on Electric Powertrain Test Bench

2017-01-2460

10/08/2017

Features
Event
International Powertrains, Fuels & Lubricants Meeting
Authors Abstract
Content
In this paper, a novel driver model is proposed to track vehicle speed in MIL (Model-in-the-Loop) test system, which has structural consistency with HIL (Hardware-in-the-Loop) test system. First, the MIL test system which contains models of driver, vehicle and test bench is established. Second, according to the connections of the established models in Matlab/Simulink environment, the vehicle speed is calculated in vehicle model. Emphatically, through the deviation between driving cycle speed and calculated vehicle speed, PI controller in driver model adjusts the vehicle speed to ideal point through sending the torque command to drive motor, the ILC (Iterative Learning Control) controller modifies and stores P value of PI controller. Then, in order to obtain the better modification of PI controller, iterative learning control algorithm is deeply researched in term of types and parameters. And the dynamic characteristic of test bench is analyzed through the shaft speed and dynamic torque of test bench. Finally, the performance of the novel driver model has been validated through the MIL test system.
The results show that under a piece of UDDS, the speed tracking accuracy can be increased by 5% on average and 20% under partial condition. The shaft speed of test bench will oscillate with the vehicle accelerated speed changing quickly in driving cycle. Besides, the oscillation period of shaft speed is about 600 ms, which reflects the torsional vibration characteristics of powertrain. The paper exerts a huge application value for further electric powertrain dynamic testing, namely improving the dynamic testing accuracy.
Meta TagsDetails
DOI
https://doi.org/10.4271/2017-01-2460
Pages
8
Citation
Liu, W., Song, Q., Li, Y., and Zhao, W., "A Novel Driver Model for Real-time Simulation on Electric Powertrain Test Bench," SAE Technical Paper 2017-01-2460, 2017, https://doi.org/10.4271/2017-01-2460.
Additional Details
Publisher
Published
Oct 8, 2017
Product Code
2017-01-2460
Content Type
Technical Paper
Language
English