Research on energy management strategy of hybrid electric vehicle considering traffic density

Journal Information

J. Hefei Univ. Tech. (Nat. Sci.), Feb.2025, Vol.48 No.2: 170-178

DOI: 10.3969/j.issn.1003-5060.2025.02.005

Authors

WU Di $ ^{1} $, ZHANG Dong $ ^{1} $, GUO Mingming $ ^{1} $, ZHAO Han $ ^{2} $, ZHANG Bingzhan $ ^{1} $, QIU Mingming $ ^{2} $

(1. School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China; 2. School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China)

Abstract and Keywords

Abstract: In this paper, the influence of traffic density on vehicle speed prediction was considered. Based on the simulation experimental platform, the driving data under different traffic densities were collected and a multi-Markov matrix vehicle speed prediction model considering the traffic density was established. The vehicle fuel economy and the state of charge (SOC) balance of the battery were taken into account as the optimization object. A hybrid electric vehicle energy management strategy based on model predictive control (MPC) was proposed, and the control strategy model was built in MATLAB/Simulink. The vehicle dynamics simulation model was built based on CRUISE software, and the co-simulation with MATLAB/Simulink was carried out. The results show that the MPC-based energy management strategy considering the traffic density improves the fuel economy significantly, which is 6.33% higher than that under the energy management strategy without considering the traffic density. This method has certain reference significance for the energy management strategy design of other hybrid electric vehicles.

Keywords: traffic density; Markov model; driving cycle identification; energy management; model predictive control(MPC)

作者信息

吴迪 $ ^{1} $，张栋 $ ^{1} $，郭鸣明 $ ^{1} $，赵韩 $ ^{2} $，张冰战 $ ^{1} $，邱明明 $ ^{2} $

(1. 合肥工业大学汽车与交通工程学院，安徽合肥 230009；2. 合肥工业大学机械工程学院，安徽合肥 230009)

摘要和关键词

摘要: 文章考虑车流密度对车速预测的影响, 基于仿真实验平台采集不同车流密度下的行驶数据, 建立考虑车流密度的多马尔可夫矩阵车速预测模型; 以整车燃油经济性和电池荷电状态(state of charge, SOC) 平衡为优化目标, 提出基于模型预测控制(model predictive control, MPC) 的混合动力汽车能量管理策略, 并在 MATLAB/Simulink 中搭建控制策略模型; 基于 CRUISE 软件搭建整车动力学仿真模型, 并与 MATLAB/Simulink 进行联合仿真。结果表明, 考虑车流密度的 MPC 能量管理策略使得整车燃油经济性有明显提高, 相较于不考虑车流密度的能量管理策略提高 6.33%。该文方法对于其他混合动力汽车的能量管理策略设计有一定的参考意义。

关键词: 车流密度；马尔可夫模型；工况识别；能量管理；模型预测控制（MPC）

Funding

安徽省中央引导地方科技扶贫示范专项资助项目(JZ2022AKKZ0409);中央高校基本科研业务费专项资金资助项目(PA2021GDSK0075)

合肥工业大学学报(自然科学版)

Navigation