Service-Oriented Real-Time Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles

This paper presents a real-time vehicle speed planning system called the real-time energy-optimal deceleration planning system (RT-EDPS). Connectivity and autonomous driving technologies that provide map and navigation data, traffic light information, and front detection sensor data are exploited to perceive and forecast upcoming deceleration events. A parameterized polynomial-based deceleration model is employed as the deceleration strategy. Real driving test data that characterize the physical limits of regenerative braking are used to model the objective function and constraints of the parameterized deceleration commands. The proposed RT-EDPS involves two speed-planning strategies with different scales of the planning horizon. Within a deceleration event horizon ascertained by the signal phase and timing data of traffic signals and 3D geographic map data, a dynamic programming-based energy-efficient deceleration strategy is used to generate long-sighted speed profiles. While the scheduled vehicle speed is being tracked, if a preceding vehicle is detected within a predefined threshold range, then a model predictive control-based energy-efficient deceleration strategy that incorporates the traffic status ahead of the ego vehicle and exploits V2V communication with the preceding vehicles is activated to re-plan the speed trajectory of the vehicle to guarantee a collision-free distance for the preceding vehicle. In virtual driving experiments, we record energy-recuperation efficiency gains of over 40% as compared to human drivers.

Automated summary

The real-time energy-optimal deceleration planning system (RT-EDPS) utilizes connectivity and autonomous driving technologies to optimize vehicle speed planning, achieving over 40% energy-recuperation efficiency gains compared to human drivers in virtual driving experiments.