Model Predictive Control with Powertrain Delay Consideration for Longitudinal Speed Tracking of Autonomous Electric Vehicles

Abstract

Accurate longitudinal control is crucial in autonomous driving, but inherent delays and lag in electric vehicle powertrains hinder precise control. This paper presents a two-stage design for a longitudinal speed controller to enhance speed tracking performance in autonomous electric vehicles. The first stage involves designing a Model Predictive Control (MPC) system that accounts for powertrain signal delay and response lag using a First Order Plus Dead Time (FOPDT) model integrated with the vehicle’s longitudinal dynamics. The second stage employs lookup tables for the drive motor and brake system to convert control signals into actual vehicle inputs, ensuring precise throttle/brake pedal values for the desired driving torque. The proposed controller was validated using the CarMaker simulator and real vehicle tests with a Hyundai IONIQ5. In real vehicle tests, the proposed controller achieved a mean speed error of 0.54 km/h, outperforming conventional PID and standard MPC methods that do not account for powertrain delays. It also eliminated acceleration and deceleration overshoots and demonstrated real-time performance with an average computation time of 1.32 ms.