Hybrid Electric Vehicle Energy Management Strategy Based on Heterogeneous Multi-agent Reinforcement Learning

被引：0

作者：

Pang, Shengzhao ^{[1
,2
]}

Zhao, Siyu ^{[1
,2
]}

Cheng, Bo ^{[1
,2
]}

Chen, Yingxue ^{[3
]}

Huangfu, Yigeng ^{[4
]}

Mao, Zhaoyong ^{[1
,2
]}

机构：

[1] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China

[2] Natl Key Lab Unmanned Aerial Vehicle Technol, Xian 710072, Peoples R China

[3] Northwestern Polytech Univ, Sch Power & Energy, Xian 710129, Peoples R China

[4] Northwestern Polytech Univ, Sch Automat, Xian 710129, Peoples R China

来源：

2024 IEEE 19TH CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, ICIEA 2024 | 2024年

基金：

中国国家自然科学基金;

关键词：

Multi-Agent Reinforcement Learning; Hybrid Electric Vehicles; Energy Management;

D O I：

10.1109/ICIEA61579.2024.10665116

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Hybrid Electric Vehicle (HEV) plays a crucial role in the transition from traditional Internal Combustion Engine (ICE) vehicles to battery electric vehicles. However, the problem of power distribution of multiple energy sources during driving is still a bottleneck restricting the development of HEVs. This paper proposes an Energy Management Strategy (EMS) based on heterogeneous multi-agent reinforcement learning to optimize the energy distribution system for a HEV that includes an ICE, a lithium battery, and a supercapacitor which can effectively match the advantages of each power source. Simulation results show the proposed strategy can better distribute the energy with similar time consumption as the traditional optimization strategy.

引用

页数：6

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