Article
Engineering, Multidisciplinary
Zheng Zhang, Chun-guang Liu, Xiao-jun Ma, Yun-yin Zhang, Lu-ming Chen
Summary: This paper presents a driving force coordination control strategy with road identification for multi-wheel distributed electric drive vehicles. By estimating tire-road forces and identifying the road friction coefficient, this control strategy can achieve accurate driving force coordination control under different driving conditions.
DEFENCE TECHNOLOGY
(2022)
Article
Materials Science, Multidisciplinary
Malal Kane, Ebrahim Riahi, Minh-Tan Do
Summary: This paper discusses the modeling of rolling resistance and the analysis of pavement texture effect, with experimental validation showing a good correlation between the model and actual results. The research also highlights the positive correlation between mean profile depth of surfaces and rolling resistance. Furthermore, it suggests the possibility of simplifying the model by neglecting the damping part in the constitutive model of rubber.
Article
Engineering, Mechanical
Jia Ye, Zhifei Zhang, Jie Jin, Ruiqi Su, Bo Huang
Summary: The tire-road friction coefficient is crucial for vehicle safety systems. Existing methods have limited accuracy, while the proposed estimation method improves accuracy by adaptively adjusting tire stiffness and accurately identifies tire damage.
NONLINEAR DYNAMICS
(2023)
Article
Engineering, Mechanical
Hongyan Guo, Xu Zhao, Jun Liu, Qikun Dai, Hui Liu, Hong Chen
Summary: An estimation framework that combines vision and vehicle dynamic information is established to accurately obtain the peak tire-road friction coefficient. The framework collects information for the road ahead from an image captured by a camera and uses a lightweight convolutional neural network to identify the road type and its corresponding range of tire-road friction coefficients. An unscented Kalman filter (UKF) method is then used to estimate the tire-road friction coefficient value directly based on the dynamic vehicle states. The results from the road-type recognition and dynamic estimation methods are synchronized, and a confidence-based fusion strategy is proposed to obtain an accurate peak tire-road friction coefficient. Virtual and real vehicle tests confirm the effectiveness of the proposed fusion estimation strategy, which outperforms both general vision-based estimation methods and dynamic-based estimation methods.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2023)
Article
Engineering, Electrical & Electronic
Xunjie Chen, Hrishikesh Sathyanarayan, Yongbin Gong, Jingang Yi, Hao Wang
Summary: This article presents a new scheme using embedded sensors to estimate the tire/road interaction. By developing physics-based and sensor models, the rubber deformation, contact pressure distribution, and relationship between longitudinal stress and external friction force are evaluated. Experimental results demonstrate the feasibility of using force-sensitive sensors to predict tire/road friction characteristics.
IEEE SENSORS JOURNAL
(2023)
Article
Engineering, Electrical & Electronic
Zhenqiang Quan, Bo Li, Shaoyi Bei, Xiaoqiang Sun, Nan Xu, Tianli Gu
Summary: This paper proposes a tire-road friction coefficient estimation method based on intelligent tire technology. Through finite element analysis and control variable method, the influence of sideslip angle on the voltage signal of each piezoelectric film under the rolling state of tire is analyzed, and the influence of load, tire pressure, vehicle speed, and slip ratio on the voltage signal of each piezoelectric film is also analyzed. Based on signal response analysis, prediction models are built and input into the brush tire model to solve the tire-road friction coefficient. The result shows that the estimation error percentage with genetic algorithm optimization is 5.14%, indicating the practicality of the friction coefficient estimation method.
SENSORS AND ACTUATORS A-PHYSICAL
(2023)
Article
Engineering, Multidisciplinary
Masahiro Higuchi, Yosuke Suzuki, Tomohiko Sasano, Hiroshi Tachiya
Summary: This study investigates a method for measuring road friction coefficients using strains on the sidewalls of tires. The proposed method is confirmed to be able to accurately measure the load acting on a tire and friction coefficient of the tire grounding surface at low speeds and under full-slip conditions.
PRECISION ENGINEERING-JOURNAL OF THE INTERNATIONAL SOCIETIES FOR PRECISION ENGINEERING AND NANOTECHNOLOGY
(2023)
Article
Engineering, Mechanical
Malal Kane, Vikki Edmondson
Summary: This paper introduces a tire/road friction prediction tool that models the tire/road contact as dynamic, viscoelastic, rough, and lubricated. The tool considers various parameters related to tire, road, contaminant, and contact operating conditions. The tool's ability to reproduce the complete curve of the tire/road friction coefficient as a function of slip rate is its main innovation. Validation through parametric studies and braking tests on different road surfaces confirms the tool's accuracy in ranking friction.
VEHICLE SYSTEM DYNAMICS
(2022)
Review
Engineering, Mechanical
Yan Wang, Jingyu Hu, Fa'an Wang, Haoxuan Dong, Yongjun Yan, Yanjun Ren, Chaobin Zhou, Guodong Yin
Summary: This study provides a comparative analysis of different methods widely utilized for TRFC estimation, including off-board sensors-based, vehicle dynamics-based, and data-driven-based methods. The research suggests that accurate knowledge of TRFC is crucial for optimizing driver maneuvers and improving the safety of intelligent vehicles.
CHINESE JOURNAL OF MECHANICAL ENGINEERING
(2022)
Article
Engineering, Mechanical
Wei Liu, Xiaowei Wang, Shuisheng Yu, Zhihao Xu
Summary: This paper investigates the tire-road friction coefficient estimation using an adaptive singular value decomposition unscented Kalman filter (ASVD-UKF) with a noise estimator. The ASVD-UKF method significantly reduces the average absolute error compared to the traditional UKF method, improving estimation accuracy. Experimental results show that the proposed ASVD-UKF method is practical and can provide a theoretical basis and experimental foundation for tire-road friction coefficient estimation.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2023)
Article
Engineering, Electrical & Electronic
Rongyun Zhang, Yongle Feng, Peicheng Shi, Linfeng Zhao, Yufeng Du, Yaming Liu
Summary: In this paper, a tire-road friction coefficient estimation algorithm is proposed for distributed drive electric vehicles using sensorless control of permanent magnet synchronous motors (PMSM). The wheel angular speed signal is replaced by the rotor speed signal obtained from PMSM sensorless control, and a T-STCKF algorithm is derived to improve accuracy. The proposed algorithm is demonstrated to be feasible through simulations and experiments, showing that it is more accurate than the STCKF algorithm.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2023)
Article
Computer Science, Information Systems
Guodong Wang, Guoxing Bai, Yu Meng, Li Liu, Qing Gu, Zhiping Ma
Summary: In this study, a hierarchical estimation method combining single-step MHE (S-MHE) and inverse tire model (ITM) based on lateral vehicle dynamics is proposed to improve the real-time performance of the estimation method of road friction coefficient (RFC) based on moving horizon estimation (MHE). The proposed method reduces the average computation time to about 0.125 s and improves the real-time performance by more than 30% while ensuring the estimation accuracy and convergence speed compared with the traditional MHE method.
Article
Engineering, Mechanical
Lulu Gao, Shite Wang, Dongyue Wang, Fei Ma, Yueqi Dong
Summary: This paper proposes a two-layer model-based method for estimating the tire-road friction coefficient for ASVs. The method utilizes dynamic models and sensor data fusion to dynamically obtain the friction coefficient and tire parameters for ASVs.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2023)
Article
Environmental Sciences
Cheng Tian, Bo Leng, Xinchen Hou, Lu Xiong, Chao Huang
Summary: This paper proposes a TRPAC fusion estimation method considering model uncertainty, which uses virtual sensing theory and gain scheduling theory to achieve accurate classification and estimation of road surface conditions. The results of simulation and real vehicle experiments show that this method has significant advantages in accuracy, convergence speed, and robustness compared to other single-source estimators.
Article
Engineering, Civil
Chunjie Li, Pan Liu, Zhenlong Xie, Zhibin Li, Huan Huan
Summary: This paper proposes a road adhesion coefficient estimation method based on vehicle-road coordination and deep learning, which can help drivers and vehicles perceive changes in road state effectively, reducing the occurrence of traffic crashes accordingly.
JOURNAL OF ADVANCED TRANSPORTATION
(2023)
