Vehicle Dynamics Estimation using Kalman Filtering Experimental Validation

Moustapha Doumiati is Assistant Professor at FBSU, Tabuk, KSA, Saudi Arabia.

Ali Charara is Full Professor- University of Technology of Compiègne (UTC), France.

Alessandro Victorino, Assistant professor at UTC, Compiègne, France.

Daniel Lechner, Research director-Ifsttar-MA, Versailles, France.

Chapter 1: Introduction

Motivation

Objective

Outline of the book

Chapter 2: Tire and vehicle modeling

Introduction

Tire dynamics

Tire forces and moments

Vertical force

Longitudinal force and longitudinal slip ratio

Lateral force and side slip angle

Aligning moment

Coupling effects between longitudinal and lateral tire forces

Tire-road friction coefficient

Normalized longitudinal traction force

Normalized lateral traction force

Quasi-static tire model

Magic tire model

Dugoff’s tire model

Linear model

Transient tire model

Wheel rotational dynamics

Static tire radius

Effective tire radius

Vehicle body dynamics

Vehicle vertical dynamics

Suspension functions

Quarter car vehicle model

Vehicle planar dynamics

1

Four-wheel vehicle model

Wheel-ground vertical forces calculation

Bicycle model

Roll dynamics and lateral load transfer evaluation

Summary

References

Chapter 3: Estimation methodology based on Kalman filtering

Introduction

State-space representation and system observability

Linear system

Nonlinear system

Estimation Method: why stochastic models?

The linear Kalman filter

Extension to the nonlinear case

The Unscented Kalman filter

Kalman filter application: Longitudinal road profile estimation

Problem description

Filter design

Experimental results: observer evaluation

Conclusion

Summary

References

Chapter 4: Vertical tire force estimation

Introduction

Algorithm description

Lateral load transfer calculation

Observer design

Observer formulation

Vertical forces estimation

Observer design

Observer formulation

Analysis concerning the two parts estimation strategy

Models observability analysis

Determining the vehicle’s mass

Experimental validation of the identification method

Detection of rollover avoidance: LTR evaluation

Experimental validation

Observers regulation

Observers evaluation

Comparison between the developed linear and nonlinear observers

Observability results

LTR evaluation

Road geometry effects

Summary

References

Chapter 5: Lateral tire force estimation

Introduction

Background on lateral force parameters

Lateral force parameters estimation

Sideslip angle estimation

Tire-road friction and cornering stiffness estimation

Effect of camber angle

Lateral force reconstruction in an open loop-scheme

Existing methods vs. proposed methods

New estimation algorithm for lateral tire force

Construction of the observer block

Vehicle model

Dynamic tire model representation

Reference lateral tire force model

Lateral force observers: state space representation

Sensitivity analysis of the sideslip angle estimation

Experimental validation

Observers tuning

Analysis and observations

Conclusion

References

Chapter 6: Embedded system for vehicle states estimation

Introduction

Experimental vehicle

Embedded sensors

Software modules

Estimation Process: VSO system

Test tracks

Test results

Conclusion

Chapter 7: Summary chapter

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