Global energy management optimization for a group of 4WD hybrid electric vehicles in connected vehicle environment

Abstract

A hierarchical energy management control strategy was proposed to realize the global optimal energy management for a group of four-wheel-drive hybrid electric vehicles (4WD HEVs) in connected vehicle environment. The higher level controller utilized signal phase and timing (SPAT) for the evaluation of the target velocities and optimal target velocity prediction objective function model was established based on model predictive control (MPC). The lower level controller adopted dynamic programming (DP) for the energy management of the vehicles. dSPACE-based simulation results validate that the higher level controller can avoid the 4WD HEVs from red light stopping and avoid collisions of the vehicles. Compared with the Gipps car following model, the fuel economy is increased by 21.4%, 21.5% and 20.6% when the lower level controller is based on DP, ECMS and the rule-based strategy, respectively. In addition, the DP-based lower level controller can achieve good velocity tracking and SOC balance. Compared with ECMS and rule-based control strategies, the average fuel economy for the group of 4WD HEVs with DP-based strategy is improved by 7.8% and 15.9% , respectively.