An energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating conditions-Reference-Cited by-同舟云学术

An energy management strategy of deep reinforcement learning based on multi-agent architecture under self-generating conditions

Published:2023-11 Issue: Volume:283 Page:128536
ISSN:0360-5442
Container-title:Energy
language:en
Short-container-title:Energy

Author:

Chang Chengcheng,Zhao Wanzhong,Wang Chunyan,Luan Zhongkai

Funder

Outstanding Youth Foundation of Jiangsu Province

Jiangsu Provincial Key Research and Development Program

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference42 articles.

1. Power management optimization of fuel cell/battery hybrid vehicles with experimental validation[J];Odeim;J Power Sources,2014

2. Classification and review of control strategies for plug-in hybrid electric vehicles[J];Wirasingha;IEEE Trans Veh Technol,2010

3. A multi-level energy management system for multi-source electric vehicles–an integrated rule-based meta-heuristic approach[J];Trovão;Appl Energy,2013

4. Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming[J];Peng;Appl Energy,2017

5. Minimum-fuel energy management of a hybrid Electric vehicle via iterative linear programming[J];Robuschi;IEEE Trans Veh Technol,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Heterogeneous multi-agent deep reinforcement learning for eco-driving of hybrid electric tracked vehicles: A heuristic training framework;Journal of Power Sources;2024-05

2. Data-Driven hierarchical energy management in multi-integrated energy systems considering integrated demand response programs and energy storage system participation based on MADRL approach;Sustainable Cities and Society;2024-04

3. Weighted fair energy transfer in a UAV network: A multi-agent deep reinforcement learning approach;Energy;2024-04