机读格式显示(MARC)
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- 008 230227s2022 sz a ob 001 0 eng d
- 035 __ |a (MiAaPQ)EBC7102408
- 035 __ |a (Au-PeEL)EBL7102408
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- 040 __ |a MiAaPQ |b eng |e rda |c MiAaPQ |d MiAaPQ
- 050 _4 |a TL145 |b .X863 2022
- 100 1_ |a Xu, Nan, |e author.
- 245 10 |a Intelligent tire systems / |c Nan Xu, Hassan Askari, and Amir Khajepour.
- 260 __ |a Cham, Switzerland : |b Springer, |c [2022]
- 300 __ |a 175 pages ; |c 24 cm
- 336 __ |a text |b txt |2 rdacontent
- 337 __ |a unmediated |b n |2 rdamedia
- 338 __ |a volume |b nc |2 rdacarrier
- 490 1_ |a Synthesis Lectures on Advances in Automotive Technology
- 504 __ |a Includes bibliographical references and index.
- 505 0_ |a 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.
- 505 8_ |a 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.
- 650 _0 |a Tires |x Performance.
- 650 _0 |a Motor vehicles |x Tires.
- 650 _0 |a Technological innovations.
- 700 1_ |a Khajepour, Amir, |e author.
- 700 1_ |a Askari, Hassan, |e author.
- 776 08 |i Print version: |a Xu, Nan |t Intelligent Tire Systems |d Cham : Springer International Publishing AG,c2022 |z 9783031102677
- 830 _0 |a Synthesis lectures on advances in automotive technology.