TY - JOUR
T1 - An Enabling Tire-Road Friction Estimation Method for Four-in-Wheel-Motor-Drive Electric Vehicles
AU - Zhang, Lei
AU - Guo, Pengyu
AU - Wang, Zhenpo
AU - Ding, Xiaolin
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2023/9/1
Y1 - 2023/9/1
N2 - In this article, a particle filter (PF)-based tire-road friction estimation method is proposed for four-in-wheel-motor-drive electric vehicles (FIWMD EVs) by synthetically using the dual global positioning system (DGPS) and three low-cost inertia measurement units (IMUs). In the scheme, two independent PF-based road friction estimators are developed for straight driving and cornering conditions. For straight driving conditions, the longitudinal tire forces are first estimated using the output torque and rotational speed of motor, based on which a tire-road friction estimator is put forward by using a particle filter and the nonlinear relationship between longitudinal tire force and tire-road friction. For cornering conditions, the lateral tire forces and vehicle sideslip angle are estimated by using three IMUs and the DGPS. A PF-based tire-road friction estimator is established based on the nonlinear lateral tire characteristics. An estimation mode decision scheme is developed to determine which of the two PF-based estimators is used to update the tire-road friction estimate by considering both tire dynamics states and tire force characteristics. The accuracy and reliability of the proposed tire-road friction estimation scheme is verified under various maneuvers and road friction conditions through hardware-in-the-loop (HIL) tests. The results show that the proposed method exhibits high estimation accuracy, robustness, and computational efficiency.
AB - In this article, a particle filter (PF)-based tire-road friction estimation method is proposed for four-in-wheel-motor-drive electric vehicles (FIWMD EVs) by synthetically using the dual global positioning system (DGPS) and three low-cost inertia measurement units (IMUs). In the scheme, two independent PF-based road friction estimators are developed for straight driving and cornering conditions. For straight driving conditions, the longitudinal tire forces are first estimated using the output torque and rotational speed of motor, based on which a tire-road friction estimator is put forward by using a particle filter and the nonlinear relationship between longitudinal tire force and tire-road friction. For cornering conditions, the lateral tire forces and vehicle sideslip angle are estimated by using three IMUs and the DGPS. A PF-based tire-road friction estimator is established based on the nonlinear lateral tire characteristics. An estimation mode decision scheme is developed to determine which of the two PF-based estimators is used to update the tire-road friction estimate by considering both tire dynamics states and tire force characteristics. The accuracy and reliability of the proposed tire-road friction estimation scheme is verified under various maneuvers and road friction conditions through hardware-in-the-loop (HIL) tests. The results show that the proposed method exhibits high estimation accuracy, robustness, and computational efficiency.
KW - Particle filter (PF)
KW - tire force estimation
KW - tire-road friction
UR - http://www.scopus.com/inward/record.url?scp=85146225912&partnerID=8YFLogxK
U2 - 10.1109/TTE.2022.3231707
DO - 10.1109/TTE.2022.3231707
M3 - Article
AN - SCOPUS:85146225912
SN - 2332-7782
VL - 9
SP - 3697
EP - 3710
JO - IEEE Transactions on Transportation Electrification
JF - IEEE Transactions on Transportation Electrification
IS - 3
ER -