Nonlinear Model Predictive Control for Preview-Based Traction Control

TL;DR

This paper introduces a nonlinear model predictive control approach for preview-based traction control in connected vehicles, leveraging tire-road friction information to improve wheel slip regulation and demonstrating real-time effectiveness through experiments.

Contribution

It develops a novel NMPC framework that incorporates tire friction preview, enhancing traction control performance in connected vehicle scenarios.

Findings

Improved wheel slip control performance with friction preview

Real-time control capability demonstrated on electric vehicle prototype

Sensitivity analysis shows performance gains across different powertrain dynamics

Abstract

This study presents a nonlinear model predictive control (NMPC) formulation for preview-based traction control, which uses the information on the expected tire-road friction coefficient ahead to enhance the wheel slip control performance, in the context of connected vehicles with V2X features. Proof-of-concept experiments on an electric vehicle prototype highlight the real-time capability of the controller, and the wheel slip control performance improvement brought by the tire-road friction coefficient preview. Finally, an experimentally validated simulation model is used in sensitivity analyses, to evaluate the performance benefit of the preview-based controller for different dynamic characteristics (e.g., time constant and pure time delays) of the electric powertrains.