Regenerative Energy Maximization Control for Autonomous Driving Electric Vehicles with Delay Compensated Obstacle Detection

As autonomous driving technology for electric vehicles advances, energy-optimized speed trajectories are crucial for enhancing the economy and practicality of electric vehicles. While numerous prior studies have focused on energy-optimized speed trajectory generation, it is necessary to verify the integration with autonomous driving systems and conduct experimental validation. This study proposes a system configuration, including external sensors, obstacle detection, optimization, and a delay-compensated controller, to achieve an energy-optimized speed trajectory. The proposed configuration is evaluated through real-world experiments, in which stopping position accuracy and regenerative energy are analyzed. Compared to the controller without delay compensation, the final position error is reduced. In addition, by properly integrating delay compensation and energy optimization, regenerative energy is increased compared to a constant deceleration trajectory. The proposed system configuration is applicable to all autonomous driving electric vehicles for conducting energy-optimization and motion-control experiments.