Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF

Area of Science:

• Automotive Engineering
• Control Systems
• State Estimation

Background:

• Accurate vehicle sideslip angle estimation is crucial for advanced driver-assistance systems (ADAS) and vehicle dynamics control.
• Traditional methods often face challenges with real-time performance and robustness under varying conditions.

Purpose of the Study:

• To develop a real-time vehicle sideslip angle state observer.
• To improve the accuracy and reliability of sideslip angle estimation using an Extended Kalman Filter (EKF) algorithm.

Main Methods:

• A 2-DOF dynamical model and tire lateral force model were used to establish the observer.
• The Extended Kalman Filter (EKF) was combined with least squares methods and a self-adapted truncation procedure.
• Jacobi matrix calculations were transformed from the time to the frequency domain.
• A self-adapted update noise estimation method and an initial value setting strategy were proposed.

Main Results:

• The proposed observer demonstrated real-time reliability in hardware-in-the-loop simulations (Matlab/Simulink-CarSim-dSPACE).
• Root Mean Square Error (RMSE) analysis verified the estimation accuracy.
• The frequency domain transformation and adaptive noise estimation contributed to improved performance.

Conclusions:

• The developed EKF-based observer provides a reliable real-time solution for vehicle sideslip angle estimation.
• The proposed methods, including frequency domain analysis and adaptive noise handling, enhance observer performance.
• This approach has significant implications for vehicle stability control and safety systems.