Li, Huan (Guangzhou Automobile Group Co., Ltd.), Wang, Jinhang (Guangzhou Automobile Group Co., Ltd.), Chen, Lihua (Guangzhou Automobile Group Co., Ltd.), Xu, Hao (Guangzhou Automobile Group Co., Ltd.), Liu, Dongsheng (Guangzhou Automobile Group Co., Ltd.), Cui, Guangri (Guangzhou Automobile Group Co., Ltd.)

Powertrain Model Based Longitudinal Velocity and Road Gradient Estimation for Four-Wheel Drive Hybrid Electric Vehicle

Scheduled for presentation during the Regular session "Vehicle Control I" (ThA2), Thursday, October 31, 2024, 11:10−11:30, Room T2

7th IFAC Conference on Engine and Powertrain Control, Simulation and Modeling, Oct 30 - Nov 1, 2024, Dalian, China

This information is tentative and subject to change. Compiled on May 16, 2025

Abstract

An estimation method for the longitudinal vehicle velocity and the road gradient is proposed. Since high order nonlinear model of the vehicle kinematics and/or tire dynamics needs determination of multiple parameters and relatively large calibration effort, an online estimation scheme based on a second-order linear powertrain model is developed. The state-coupling of road gradient and velocity is tackled to guarantee estimation performance in extreme driving conditions such as all wheel slip, where the gradient and velocity both have strong transient. Variable estimation gains are scheduled by operational conditions to improve estimation robustness. The algorithm is implemented on a four-wheel drive plug-in hybrid electric vehicle and tested on different road conditions including ice and snow road as well as concrete pavement. The root mean square error and maximum error of the observer are less than 3 km/h and 6 km/h, respectively.