TY - JOUR
T1 - Modular scheme for four-wheel-drive electric vehicle tire-road force and velocity estimation
AU - Guo, Hongyan
AU - Liu, Hui
AU - Yin, Zhenyu
AU - Wang, Yulei
AU - Chen, Hong
AU - Ma, Yingjun
N1 - Publisher Copyright:
© The Institution of Engineering and Technology 2018.
PY - 2019/3/1
Y1 - 2019/3/1
N2 - The limited availability of vehicle state information, including tire-road forces and vehicle velocities, restricts the development of control strategies for intelligent vehicles and new energy vehicles. This study proposes a modular estimation scheme for tire-road forces and vehicle velocities that can effectively cope with the cyclic coupling of the vehicle dynamics. The longitudinal tire-road forces are estimated using a sliding mode observer. Then, an observer for the lateral tire-road forces that exist in a cascade structure with the longitudinal tire-road force observer is designed. Non-linear vehicle velocity observers that take the estimated longitudinal and lateral tire-road forces as inputs are designed. A genetic algorithm approach is employed to select the observer gains. Finally, experimental validations under normal conditions and offline simulations under critical conditions for verifying the robustness with respect to measurement noise are conducted. The results demonstrate that the proposed modular scheme for.
AB - The limited availability of vehicle state information, including tire-road forces and vehicle velocities, restricts the development of control strategies for intelligent vehicles and new energy vehicles. This study proposes a modular estimation scheme for tire-road forces and vehicle velocities that can effectively cope with the cyclic coupling of the vehicle dynamics. The longitudinal tire-road forces are estimated using a sliding mode observer. Then, an observer for the lateral tire-road forces that exist in a cascade structure with the longitudinal tire-road force observer is designed. Non-linear vehicle velocity observers that take the estimated longitudinal and lateral tire-road forces as inputs are designed. A genetic algorithm approach is employed to select the observer gains. Finally, experimental validations under normal conditions and offline simulations under critical conditions for verifying the robustness with respect to measurement noise are conducted. The results demonstrate that the proposed modular scheme for.
UR - http://www.scopus.com/inward/record.url?scp=85062665974&partnerID=8YFLogxK
U2 - 10.1049/iet-its.2018.5098
DO - 10.1049/iet-its.2018.5098
M3 - Article
AN - SCOPUS:85062665974
SN - 1751-956X
VL - 13
SP - 551
EP - 562
JO - IET Intelligent Transport Systems
JF - IET Intelligent Transport Systems
IS - 3
ER -