Parameterized Energy-Optimal Regenerative Braking Strategy for Connected and Autonomous Electrified Vehicles: A Real-Time Dynamic Programming Approach

TL;DR

This paper introduces a real-time, parameterized deceleration planning system for connected autonomous electric vehicles that maximizes regenerative braking energy while ensuring smooth and physically feasible speed profiles.

Contribution

It develops a polynomial-based deceleration model and a dynamic programming approach to optimize energy recovery in autonomous electric vehicles under real-time constraints.

Findings

Longer preview distances increase energy recuperation.

EDPS improves energy recovery compared to traditional methods.

Autonomous driving with EDPS reduces trip time while maximizing energy savings.

Abstract

This paper presents a vehicle speed planning system called the energy-optimal deceleration planning system (EDPS), which aims to maximize energy recuperation of regenerative braking of connected and autonomous electrified vehicles. A recuperation energy-optimal speed profile is computed based on the impending deceleration requirements for turning or stopping at an intersection. This is computed to maximize the regenerative braking energy while satisfying the physical limits of an electrified powertrain. In automated driving, the powertrain of an electrified vehicle can be directly controlled by the vehicle control unit such that it follows the computed optimal speed profile. To obtain smooth optimal deceleration speed profiles, optimal deceleration commands are determined by a parameterized polynomial-based deceleration model that is obtained by regression analyses with real vehicle driving test data. The parameters are dependent on preview information such as residual time and distance as well as target speed. The key design parameter is deceleration time, which determines the deceleration speed profile to satisfy the residual time and distance constraints as well as the target speed requirement. The bounds of deceleration commands corresponding to the physical limits of the powertrain are deduced from realistic deceleration test driving. The state constraints are dynamically updated by considering the anticipated road load and the deceleration preference. For validation and comparisons of the EDPS with different preview distances, driving simulation tests with a virtual road environment and vehicle-to-infrastructure connectivity are presented. It is shown that the longer preview distance in the EDPS, the more energy-recuperation. In comparison with driver-in-the-loop simulation tests, EDPS-based autonomous driving shows improvements in energy recuperation and reduction in trip time.