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.
Highlights
In recent years, battery electric vehicles (BEVs) have become prevalent as they improve air quality in urban areas
In addition to BEVs, hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and fuel-cell electric vehicles (FCEVs) are the most popular electrified vehicles powered by dual power sources—an engine and an electric motor— for vehicle traction
We present an optimal control problem to find an energy-optimal speed profile maximizing the recuperated energy obtained from electrified vehicles
Summary
Battery electric vehicles (BEVs) have become prevalent as they improve air quality in urban areas. 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 This parameterized energy-optimal speed planning strategy is useful for reducing the computation time because it does not require any state or input quantization, which is the main difference from existing dynamic programming approaches to energy-optimal speed planning. Data of real vehicle driving tests for various deceleration scenarios to determine the upper and lower bounds of the deceleration time determining the features of deceleration speed profiles such as peak deceleration and smoothness Using this approach to set the bounds of deceleration commands makes the proposed parameterized speed planning strategy more practical compared with other optimal control approaches.
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