Multi-Agent Deep Reinforcement Learning-Based Multi-Objective Cooperative Control Strategy for Hybrid Electric Vehicles-Reference-Cited by-同舟云学术

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Multi-Agent Deep Reinforcement Learning-Based Multi-Objective Cooperative Control Strategy for Hybrid Electric Vehicles

Published:2024-08 Issue:8 Volume:73 Page:11123-11135
ISSN:0018-9545
Container-title:IEEE Transactions on Vehicular Technology
language:
Short-container-title:IEEE Trans. Veh. Technol.

Author:

Gan Jiongpeng¹^ORCID,Li Shen²^ORCID,Lin Xianke³^ORCID,Tang Xiaolin¹^ORCID

Affiliation:

1. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, China

2. Department of Civil Engineering, Tsinghua University, Beijing, China

3. Department of Mechanical Engineering, University of Ontario Institute of Technology, Oshawa, ON, Canada

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/25/10637911/10461090.pdf?arnumber=10461090

Reference32 articles.

1. State of the Art and Trends in Electric and Hybrid Electric Vehicles

2. Cooperative Control of Drive Motor and Clutch for Gear Shift of Hybrid Electric Vehicles With Dual-Clutch Transmission

3. Design of a hybrid energy management system using designed rule‐based control strategy and genetic algorithm for the series‐parallel plug‐in hybrid electric vehicle

4. Fuzzy logic and Elman neural network tuned energy management strategies for a power-split HEVs

5. Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon

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