  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.
