Energy-Optimal Deceleration Planning System for Regenerative Braking of Electrified Vehicles with Connectivity and Automation

This paper presents an energy-optimal deceleration planning system (EDPS) to maximize regenerative energy for electrified vehicles on deceleration events perceived by map and navigation information, machine vision and connected communication. The optimization range for EDPS is restricted within an upcoming deceleration event rather than the entire routes while in real time considering preceding vehicles. A practical force balance relationship based on an electrified powertrain is explicitly utilized for building a cost function of the associated optimal control problem. The optimal inputs are parameterized on each computation node from a set of available deceleration profiles resulting from a deceleration time model which are configured by real-world test drivings. Also, to maximize energy recuperation and avoid front collision and jittering, the proposed EDPS uses a hierarchical control architecture with two layers: long-sighted planning system considering the entire scope of deceleration events and real-time replanning system considering the run-time look-ahead information. Experiments which are based on a real-world driving data obtained from a plug-in hybrid vehicle (PHEV) indicate that the regenerative energy of EDPS has been improved over average 33 % when comparing to the existing system driven by a human driver without connectivity and automation benefits, and the results also show that EDPS generates feasible deceleration profiles.