An Adaptive Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles During Car-Following Process

Jiaqi Xue, Xiongxiong You, Xiaohong Jiao, Yahui Zhang
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引用次数: 3

Abstract

An adaptive energy management control strategy is proposed for a commuter plug-in hybrid electrical vehicle (PHEV) during car-following process in this paper. The proposed energy management strategy (EMS) is an instantaneous optimization control strategy integrating car-following behavior performance index into adaptive equivalent consumption minimization strategy (A-ECMS). In order to achieve better fuel economy and safety performance under different car-following scenarios, the equivalent factor (EF) of ECMS and the weight factor of car-following performance in the instantaneous optimization cost function are designed as adaptive forms of Map tables about battery state of charge (SOC) and travel distance. The Mapping tables are established offline by utilizing historical traffic data of the commute road and particle swarm optimization (PSO) method. The effectiveness and practicality of the designed EMS are verified through the co-simulation of MATLAB/Simulink and GT-Suite simulator.
插电式混合动力汽车跟车过程中的自适应能量管理控制策略
针对插电式混合动力汽车的跟车过程,提出了一种自适应能量管理控制策略。提出的能量管理策略(EMS)是一种将汽车跟随行为性能指标与自适应等效消耗最小化策略(A-ECMS)相结合的瞬时优化控制策略。为了在不同的跟车场景下获得更好的燃油经济性和安全性能,将ECMS等效因子(EF)和瞬时优化成本函数中跟车性能权重因子设计为关于电池荷电状态(SOC)和行驶距离的自适应Map表形式。利用通勤道路历史交通数据,采用粒子群算法建立离线映射表。通过MATLAB/Simulink和GT-Suite模拟器的联合仿真,验证了所设计的EMS的有效性和实用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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