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

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. 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 design 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 time-varying bounds of deceleration commands corresponding to the physical limits of the powertrain are deduced from realistic deceleration test driving. 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.

Abstract licence: CC BY-NC-ND

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