Shen, Daliang (Argonne National Laboratory), Vliem, Sam J.M. (Eindhoven University of Technology), Jeong, Jongryeol (Argonne National Laboratory), Russo, Miriam (Argonne National Laboratory), Das, Debashis (Argonne National Laboratory), Donkers, M.C.F. (Tijs) (Eindhoven University of Technology), Karbowski, Dominik (Argonne National Laboratory)

Testing Road Grade-Aware Benefits of Koopman Model Predictive Control for Energy-Efficient Driving

Scheduled for presentation during the Regular Session "Optimization and control for eco-driving and racing" (MoA2), Monday, June 16, 2025, 12:40−13:00, 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 May 31, 2025

Abstract

This study presents the implementation and evaluation of a road grade-aware control strategy for energy-efficient driving on the Cadillac Lyriq Electric Vehicle (EV) as a test platform. Leveraging a Koopman Model Predictive Control (KMPC) framework, the developed control approach integrates real-time road grade variations, approximated through Legendre polynomial-based modeling, into its predictive optimization. A dynamometer-based Vehicle-in-the-Loop (VIL) testing setup was employed to rigorously assess the efficacy of the control system across a range of scenarios characterized by varying road grade profiles and target cruising speeds. Comparative analyses with baseline controls highlight significant energy savings, with KMPC effectively utilizing downhill momentum for kinetic energy storage, subsequently enhancing uphill driving efficiency. This paper provides comprehensive details of the control system design, its dynamometer-based validation, and the quantified energy and time metrics, demonstrating the practical viability of the grade-aware strategy in improving EV driving efficiency.