Vehicle performance is largely controlled by the tire dynamic characteristics mediated by forces and moments generated at the tire-road contact patch. The tire may undergo deformations that increase the longitudinal and lateral forces within the contact patch. It is crucial to develop a model for the accurate prediction of tire characteristics, as this will enable optimization of the overall performance of vehicles. Research has been conducted to identify new strategies for tire measurement and modeling vehicle dynamics analysis. Autonomous vehicles (AVs), electric vehicles (EVs), shared sets, and connected vehicles have further revolutionized interdisciplinary research on vehicle and tire systems. The performance and reliability of vehicle active safety and advanced driver assistance systems (ADASs) are primarily influenced by the tire force capacity, which cannot be measured. High active safety and optimized ADAS are particularly crucial for automated driving systems (ADS) to guarantee passenger safety in intelligent transportation settings. The establishment of online measurement or estimation tools for tire states, especially for autonomous vehicles, is critical.
Author(s): Nan Xu, Hassan Askari, Amir Khajepour
Series: Synthesis Lectures on Advances in Automotive Technology
Publisher: Springer
Year: 2022
Language: English
Pages: 174
City: Cham
Preface
Contents
About the Authors
1 Introduction to Intelligent Tires
1.1 Introduction
1.2 Advantages of Intelligent Tires
1.3 Evolution and Prospective Research on Intelligent Tires
1.4 Structure of the Book
2 Tire Modeling
2.1 Introduction
2.2 Brush Model with Rigid Carcass
2.2.1 Tire Modeling Basics
2.2.2 Brush Model Under Combined Longitudinal and Lateral Slip Conditions
2.3 Theoretical Model Considering a Flexible Carcass
2.3.1 The Direction of the Resultant Shear Force
2.3.2 Refined Tire Model
2.3.3 Model Simulation and Characteristics Analysis
2.4 UniTire Semi-empirical Model
2.4.1 UniTire Basic Equations
2.4.2 Semi-empirical Modeling For Combined Conditions with Anisotropic Tire Slip Stiffness
2.5 Summary
3 Sensing Systems in Intelligent Tires
3.1 Types of Sensors in Intelligent Tires
3.1.1 Optical Sensors
3.1.2 Capacitive Sensors
3.1.3 Optical Fiber Sensors
3.1.4 Surface Acoustic Wave Sensors
3.1.5 Magnetic Sensors
3.1.6 Ultrasonic Distance Sensors
3.1.7 Microelectromechanical System Acceleration Sensors
3.2 Types of Energy Harvesting Technology
3.3 Summary
4 Tire Forces Estimation in Intelligent Tire
4.1 Introduction
4.2 Experimental Design and Data Analysis
4.2.1 Experimental Design
4.2.2 Frequency Domain Analysis
4.2.3 Time Domain Analysis
4.3 Physical Model-Based Tire Force Estimation Method
4.3.1 Vertical Force Estimation
4.3.2 Lateral Force Estimation
4.4 Machine Learning in the Tire Industry
4.5 Different Machine Learning Algorithms for Tire Force Estimation
4.5.1 Data Preprocessing for Tire Forces Estimation
4.5.2 Prediction Result Comparison for Different Algorithms
4.6 Summary
Bibliography
5 Machine Learning for Slip Angle and Slip Ratio Predictions
5.1 Introduction
5.2 Data Analysis
5.2.1 Slip Angle Variation Effects
5.3 Physical-Model-Based Slip Angle Estimation Algorithm
5.4 Different Machine Learning Algorithms for Tire Slip Angle Estimation
5.4.1 Data Preprocessing
5.4.2 Prediction Results Comparison of Different Algorithms
5.5 Different Machine Learning Algorithms for Tire Slip Ratio Estimation
5.5.1 Data Preprocessing
5.5.2 Prediction Results Comparison of Different Algorithms
5.5.3 Ten-Fold Cross-Validation
5.6 Summary
6 Tire-Road Friction Estimation
6.1 Introduction
6.2 Estimation of the Aligning Torque and Pneumatic Trail
6.2.1 Data Analysis
6.2.2 Aligning Torque Estimation
6.2.3 Pneumatic Trail Estimation
6.3 Friction Coefficient Estimation
6.3.1 Estimation of the Friction Coefficient for Longitudinal Slip Conditions
6.3.2 Estimation of the Friction Coefficient for Cornering Conditions
6.4 Summary
