Online Estimation and Control Method of Optimal Slip Ratio for Vehicle With an Electric Power-Assisted Brake System

In the context of intelligentization and electrification, the electric power-assisted brake system is an optimal choice for vehicle, allowing precise slip ratio control through oil pressure adjustment to enhance braking effectiveness. Estimating the optimal slip ratio is critical for slip ratio control. Current algorithms for estimating the optimal slip ratio is still room for improving the accuracy and efficiency, especially under the changing road surface, introducing significant lag. This research proposes a self-adaptive optimal slip ratio estimation method for road surfaces and a slip ratio control strategy. Firstly, the Burckhardt tire model is adjusted to better represent the utilizing adhesion coefficient-slip ratio characteristic of the tire actually used. Then a tire model parameter identification algorithm based on road surface similarity and Adam gradient descent is developed. During the identification, the initial value selection method and the segmented mean method are combined to improve accuracy and efficiency. Additionally, decision tree is employed to detect the road surface change, with the sample data elimination and compensation methods, achieving less lag in road surface adaptation for optimal slip ratio estimation. Finally, the deviation between the actual and optimal slip ratio is taken to determine the target oil pressure. Coupled with single neuron PID controller, the slip ratio of vehicle is effectively controlled. Results from the Hardware-in-the-Loop experiments conducted on different road surfaces demonstrate that the proposed optimal slip ratio estimation algorithm achieves superior accuracy and efficiency. Additionally, compared to sliding mode control, the proposed slip ratio control strategy exhibits enhanced robustness.