基于分层式控制的混合动力汽车生态驾驶研究

Study on Eco-driving of PHEVS Based on Hierarchical Control Strategy

智能交通系统技术的发展为进一步提高车辆驾驶性能带来了新的机遇。插电式混合动力汽车的生态驾驶涉及到3个问题,分别为如何利用动态交通信息对纵向行驶速度进行规划,动力电池SOC全局最优快速规划,以及动力系统实时能量管理。为此,本文中设计了一种结合通精度模型的兼顾计算效率与求解精度的分层式控制策略。上层控制融合了动态交通信号灯信息,通过对车辆行驶速度优化提高了驾驶舒适性,中层则通过对动力系统模型拟合近似,利用凸优化算法实现了SOC快速全局最优规划,为消除拟合模型产生的误差,下层则基于原始非线性模型,通过反馈控制,构建了一种自适应等效燃油消耗最小值策略(A-ECMS)。结果表明,车辆的驾驶舒适性相比于没有速度优化的策略提升了75.92%,且燃油经济性相比于两种常用的基于线性规划的策略分别提升了7.39%与10.91%。

The development of intelligent transportation system technology provides a great opportunity to further improve the driving performance of automotive vehicles.The eco-driving of plug-in hybrid electric vehicles(PHEV)involves three issues,namely,how to use dynamic traffic information for longitudinal driving speed planning,optimal rapid planning of global battery state of charge(SOC),and the real-time energy management of the power system.This paper devises a hierarchical control strategy that combines the accuracy model with both calculation efficiency and solution accuracy to solve these problems.In the upper control layer,dynamic traffic light signal information is incorporated into the velocity optimization process to improve driving comfort.In the middle control layer,the SOC fast global optimal planning is realized based on the convex optimization by fitting the powertrain model.Finally,in order to eliminate the error caused by the fitting model,based on the original nonlinear model,an adaptive equivalent consumption minimization strategy(A-ECMS)is established in the lower control layer through feedback control.The results show that the driving comfort is improved by 75.92%compared with the strategy without optimization in velocity,and the fuel economy is improved by 7.39%and 10.91%respectively compared with that of two often used linear programming-based energy management strategies(EMSs).