Real-Time Dynamic Adhesion Coefficient Estimation and BP Neural Network-Optimized Lateral Stability Control for Distributed-Drive Electric Vehicles

To address the instability issues of distributed-drive electric vehicles (DDEV) operating on roads with abrupt changes in adhesion coefficients, a lateral stability control strategy and torque distribution method based on backpropagation (BP) neural network optimization were proposed. First, an Unscented Kalman Filter (UKF) estimation algorithm incorporating real-time variation detection of adhesion coefficients is developed. To ensure rapid response and accurate estimation of current adhesion coefficients during sudden road condition changes, threshold-based real-time detection of adhesion coefficient fluctuations is introduced. Second, a hierarchical stability control strategy specifically designed for varying adhesion coefficient conditions is established. The upper-layer controller employs a Bat Algorithm (BA) optimized BP neural network, which takes the sideslip angle and yaw rate as control targets to calculate the required yaw moment for vehicle stabilization, thereby enhancing real-time computational efficiency and solution accuracy. The lower-layer controller utilizes the estimated road adhesion coefficients to implement a quadratic programming algorithm, optimizing wheel torque distribution with the objective of minimizing tire load rate. Finally, a co-simulation platform is constructed using Carsim/Simulink for validation. The results demonstrate that the proposed estimation algorithm can precisely estimate road adhesion coefficients under extreme conditions of abrupt coefficient changes; The developed stability controller significantly enhances both handling stability and driving stability of DDEV.