de Vries, Wytze (Eindhoven University of Technology), van Kampen, Jorn (Eindhoven University of Technology), Salazar, Mauro (Eindhoven University of Technology)

A Human-Optimized Model Predictive Control Scheme and Extremum Seeking Parameter Estimator for Slip Control of Electric Race Cars

Scheduled for presentation during the Regular Session "Vehicle dynamics " (WeB2), Wednesday, June 18, 2025, 15:20−15:40, Jos

AAC 2025 11th IFAC International Symposium on Advances in Automotive Control, June 15-18, 2025, Eindhoven, Netherlands

This information is tentative and subject to change. Compiled on June 1, 2025

Abstract

This paper presents a longitudinal slip control system for a rear-wheel-driven electric endurance race car. The control system integrates Model Predictive Control (MPC) with Extremum Seeking Control (ESC) to optimize the traction and regenerative braking performance of the powertrain. The MPC contains an analytical solution which results in a negligible computation time, whilst providing an optimal solution to a multi-objective optimization problem. The ESC algorithm allows continuous estimation of the optimal slip reference without assuming any prior knowledge of the tire dynamics. Finally, the control parameters are determined using a human-driven preference-based optimization algorithm in order to obtain the desired response. Simulation results and comparisons with other methods demonstrate the system’s capability to automatically determine and track the optimal slip values, showing stability and performance under varying conditions. This paper serves a summary of de Vries et al. (2024).