Real time dynamic adhesion coefficient estimation and BP neural network optimized lateral stability control for
Zhigang Zhou1, Ruili Yang2, Fang Xu3
1College of Vehicle and Traffic Engineering, Henan University of Science and technology, Luoyang, 410300, China.
Scientific Reports
|December 5, 2025
Summary
This study introduces a novel stability control system for distributed-drive electric vehicles (DDEV) that accurately estimates changing road conditions. The enhanced system improves vehicle handling and driving stability on varied surfaces.
Area of Science:
- Automotive Engineering
- Control Systems
- Artificial Intelligence
Background:
- Distributed-drive electric vehicles (DDEV) face instability on roads with fluctuating adhesion.
- Accurate estimation of real-time adhesion coefficients is crucial for vehicle stability.
Purpose of the Study:
- To develop a robust lateral stability control strategy and torque distribution method for DDVEs.
- To enhance vehicle stability and handling on roads with abrupt changes in adhesion coefficients.
Main Methods:
- An Unscented Kalman Filter (UKF) with real-time adhesion coefficient variation detection was employed.
- A hierarchical control strategy utilized a Bat Algorithm (BA)-optimized backpropagation (BP) neural network for yaw moment calculation.
- Quadratic programming optimized wheel torque distribution based on estimated adhesion coefficients.
Main Results:
- The UKF algorithm accurately estimated adhesion coefficients even under extreme, abrupt changes.
- The proposed stability controller significantly improved both handling and driving stability in simulations.
- The BA-optimized BP neural network enhanced computational efficiency and accuracy.
Conclusions:
- The developed control strategy effectively addresses DDEV instability on roads with varying adhesion.
- The integrated approach of advanced estimation and hierarchical control offers a promising solution for DDEV safety.
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