车联网环境下四驱混合动力汽车队列能量管理全局优化

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

为实现四驱混合动力汽车队列的能量管理全局优化,本文提出一种车联网环境下的分层能量管理控制方法,上层控制器基于交通信号灯正时,求解混合动力汽车的目标车速;基于模型预测,建立最优目标车速预测目标函数模型,下层控制器基于动态规划(dynamic programming,DP),进行四驱混合动力汽车能量管理全局优化,基于dSAPCE的仿真结果表明,本文提出的上层控制方法可以有效地避免四驱混合动力汽车红灯停车及发生碰撞;相比于Gipps跟车模型,下层控制器基于DP,ECMS和基于规则的时,采用本文提出的上层控制方法得到平均百公里油耗分别降低21.4%,21.5%和20.6%.此外,下层控制器基于DP时能够实现良好的车速跟随以及动力电池SOC均衡,且相对于ECMS和基于规则的控制方法,四驱混合动力汽车队列平均燃油经济性分别提高7.8%和15.9%.

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 of4WD HEVs with DP-based strategy is improved by 7.8% and 15.9%, respectively.