A Nonlinear Slip Ratio Observer Based on ISS Method for Electric Vehicles

2018-01-0557

04/03/2018

Event
WCX World Congress Experience
Authors Abstract
Content
Knowledge of the tire slip ratio can greatly improve vehicle longitudinal stability and its dynamic performance. Most conventional slip ratio observers were mainly designed based on input of non-driven wheel speed and estimated vehicle speed. However, they are not applicable for electric vehicles (EVs) with four in-wheel motors. Also conventional methods on speed estimation via integration of accelerometer signals can often lead to large offset by long-time integral calculation. Further, model uncertainties, including steady state error and unmodeled dynamics, are considered as additive disturbances, and may affect the stability of the system with estimated state error. This paper proposes a novel slip ratio observer based on input-to-state stability (ISS) method for electric vehicles with four-wheel independent driving motors. Instead of estimating vehicle speed, the proposed method employs the estimated error of motor torque as the correction output by taking the advantage of electric vehicles that the torque of the driving motors can directly reflect the tire force. Also vehicle acceleration is directly used as a time-varying parameter of the system to reflect the longitudinal dynamic characteristics of the vehicle. The error dynamics is input-to-state stable subject to the disturbances, such that the nonlinear longitudinal characteristics of each tire can be effectively dealt with. Some extensive simulation has been conducted to verify the proposed slip ratio observer with an AMESim-based electric vehicle model. The results show that the designed nonlinear slip ratio observer has the better performance compared with the conventional EKF method.
Meta TagsDetails
Pages
8
Citation
Ren, B., Deng, W., Chen, H., and Wang, J., "A Nonlinear Slip Ratio Observer Based on ISS Method for Electric Vehicles," SAE Technical Paper 2018-01-0557, 2018, .
Additional Details
Publisher
Published
Apr 3, 2018
Product Code
2018-01-0557
Content Type
Technical Paper
Language
English