A Virtual Sensing Module for Optimal Chassis Control: Tire Forces, SideSlip Angle, and Road Grip Inference

Automated and electrified ground vehicles depend on optimal chassis control to synergistically orchestrate different actuators. This integration aims to improve performance, comfort, handling, and stability. However, this optimal control problem necessitates several key parameters that are expensive or impractical to measure. Virtual sensing emerges as an efficient and cost-effective alternative, utilizing existing vehicle sensors to overcome these challenges. The present study proposes a Virtual Sensing Module (VSM) designed to cope with a chassis controller. It is capable of estimating tire forces, the vehicle's SideSlip Angle (SSA), and the Tire-Road Friction Coefficient (TRFC). Furthermore, the VSM has the benefit of inferring the vehicle mass and adjusting the tire parameters. Importantly, the VSM operates effectively across coupled dynamics and under both standard and aggressive driving conditions. The proposed framework adopts a Data to Features to Decision structure: The Feature segment tracks the tire forces and SSA via Model-Based (MB) dynamic estimators. Fed by the outputs of the previous sub-module, the Decision block tackles the TRFC estimation challenge using a novel MB strategy. This strategy was benchmarked against various Data-Driven techniques for validation. The effectiveness of the VSM was verified through both simulation and real-world experimental data under varying road conditions. The findings demonstrate the module's high accuracy, with minimal estimation errors, and its efficiency in both low and high excitation scenarios.