Co-Operative Adaptive Cruise Control Design for the McMaster EcoCAR Cadillac Lyriq

This paper presents the implementation of a cooperative adaptive cruise control algorithm (CACC) designed as part of the EcoCAR EV Challenge. The controller will be implemented in a modified 2023 Cadillac Lyriq with the stock advanced driver assistance system (ADAS) disabled. The key innovation in this system includes a fuzzy logic controller which will determine a weighting factor to generate a desired torque output from two independent PID controllers, maintaining either a time-based distance gap to the lead vehicle, or the dynamic target speed. Cruise control without a lead vehicle is handled by jerk-limited S-curve optimization. The objective of these metrics is to improve driver comfort and increase overall vehicle range while prioritizing driver safety. Performance of this adaptive cruise control (ACC) algorithm is evaluated both in a Software-In-Loop (SIL) environment using simulation software, and Vehicle-In-Loop (VIL) where the algorithm is tested on a closed track. The evaluation is done using standardized drive cycles such as the Highway Fuel Economy Driving Schedule (HWFET) and Federal Test Procedure (FTP). These drive cycles were specifically chosen to expose the system to many different scenarios, allowing for a robust system that can safely react to various driver scenarios.