Hierarchical eco-driving control strategy for connected automated fuel cell hybrid vehicles and scenario-/hardware-in-the loop validation

被引：2

作者：

Zhang, Yahui ^{[1
,3
,4
]}

Wei, Zeyi ^{[1
]}

Wang, Zhong ^{[2
]}

Tian, Yang ^{[1
,3
]}

Wang, Jizhe ^{[1
]}

Tian, Zhikun ^{[1
]}

Xu, Fuguo ^{[5
]}

Jiao, Xiaohong ^{[1
]}

Li, Liang ^{[5
]}

Wen, Guilin ^{[1
]}

机构：

[1] Yanshan Univ, Sch Mech Engn, Qinhuangdao 066004, Peoples R China

[2] Yanshan Univ, Sch Vehicle & Energy, Qinhuangdao 066004, Peoples R China

[3] Yanshan Univ, Natl Key Lab Hoisting Machinery Key Technol, Qinhuangdao 066004, Peoples R China

[4] Tsinghua Univ, State Key Lab Automot Safety & Energy, Beijing 100084, Peoples R China

[5] Chiba Univ, Grad Sch Engn, Chiba 2638522, Japan

来源：

ENERGY | 2024年 / 292卷

基金：

中国国家自然科学基金;

关键词：

Fuel cell hybrid vehicles; Velocity planning; Energy management; Hierarchical framework; Model predictive control; Connected scenario; ENERGY MANAGEMENT STRATEGY; ELECTRIC VEHICLES; OPTIMIZATION; POWER;

D O I：

10.1016/j.energy.2024.130592

中图分类号：

O414.1 [热力学];

学科分类号：

摘要：

A reliable and real-time eco-driving control strategy that incorporates vehicle connectivity is crucial for enhancing the fuel economy and mobility of fuel cell hybrid vehicles (FCHVs). However, the strong coupling of dynamic traffic, vehicle dynamics, and powertrain characteristics makes eco-driving challenging in terms of effectiveness and computational efficiency for co-optimizing velocity planning and energy management. This study proposes a hierarchical eco-driving predictive control framework for connected automated FCHVs, which improves comprehensive performance and is real-time applicable by incorporating an explicit dynamic traffic model (EDTM)-based velocity planning and an equivalent consumption minimization strategy (ECMS). The EDTM predicts the ego vehicle's state of proceeding through signalized intersections in dynamic traffic scenarios. The model predictive control is employed for multi-objective velocity planning, which balances energy savings, comfort, and traffic efficiency. At the powertrain level, a multi-horizon predictive ECMS (MhPECMS) is designed to incorporate both the optimized velocity (short-horizon) and the EDTM-based predictive velocity (mid-horizon). Dynamic traffic Scenario- and Hardware-in-the-Loop (S/HiL) validations show that the proposed strategy can make driving smoother in vehicle-following and through signalized intersections, and markedly improve fuel economy by 5.41% compared to the baseline controller. This study helps provide valuable insight into improving the efficiency and mobility of connected vehicles.

引用

页数：18