Powertrain Model based Longitudinal Velocity and Road Gradient Estimation for Four-wheel Drive Hybrid Electric Vehicle

被引：0

作者：

Li, Huan ^{[1
]}

Wang, Jinhang ^{[1
]}

Chen, Lihua ^{[1
]}

Xu, Hao ^{[1
]}

Liu, Dongsheng ^{[1
]}

Cui, Guangri ^{[1
]}

机构：

[1] Guangzhou Automobile Grp Co Ltd, Guangzhou 511434, Guangdong, Peoples R China

来源：

IFAC PAPERSONLINE | 2024年 / 58卷 / 29期

关键词：

four-wheel drive; hybrid electric vehicle; velocity estimation; road gradient; variable gain;

D O I：

10.1016/j.ifacol.2024.11.125

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. Copyright (c) 2024 The Authors.

引用

页码：88 / 93

页数：6

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