Intelligent tire systems / Nan Xu, Hassan Askari, and Amir Khajepour.

Format:

Book

Author/Creator:

Xu, Nan, author.

Khajepour, Amir, author.

Askari, Hassan, author.

Series:

Synthesis Lectures on Advances in Automotive Technology

Language:

English

Subjects (All):

Tires--Performance.

Tires.

Motor vehicles--Tires.

Motor vehicles.

Technological innovations.

Physical Description:

1 online resource (175 pages)

Edition:

1st ed.

Place of Publication:

Cham, Switzerland : Springer, [2022]

Summary:

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.

Contents:

Intro

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.

Notes:

Includes bibliographical references and index.

Description based on print version record.

Description based on publisher supplied metadata and other sources.

Other Format:

Print version: Xu, Nan Intelligent Tire Systems

ISBN:

3-031-10268-1

OCLC:

1347738493