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 resulted from map information and connected communication. The optimization range for EDPS is restricted within an upcoming deceleration event rather than the entire routes while considering vehicles driving in front of ego-vehicle. The EDPS is an ecological driver assistance system with level 2 or 3 automation since acceleration is operated by an adaptive cruising system or a human driver and deceleration is operated on a unit of deceleration events which are divided into static ones such as turning and warning as well as dynamic ones such as traffic light. The event-based optimal deceleration profile is obtained by a dynamic programming framework including a driving motor performance model and a gear box model, and with the detection of a front vehicle the profile is updated in real time by nonlinear model predictive control scheme which considers a connected configuration and a modified intelligent driver model. The performance of EDPS has been rigorously validated both based on real-world driving data sets. The numerical experiments indicate the regenerative energy of EDPS has been improved over average 60 % when compared to the existing system operated without connectivity and automation (CA) benefits while simultaneously illustrating feasible deceleration profiles referring to vehicle deceleration feature model. To identify real-time performance on real-world driving environment, EDPS is also validated in Hardware in the loop testbed to implement realistic dynamic scenarios and field tests.