Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor

Shenyuan Liu; Zhiming Wu; Xiaokai Chen; Zhengyu Li

doi:10.1109/ICMSP58539.2023.10170967

Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor

Shenyuan Liu, Zhiming Wu, Xiaokai Chen^*, Zhengyu Li

^*此作品的通讯作者

机械与车辆学院

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

1 引用（Scopus）

摘要

Interest in hybrid electric agricultural tractors (HEAT) is growing as traditional agricultural tractors consume large amounts of non-renewable resources. Energy management strategy (EMS) plays an important role in improving fuel consumption. This paper studies a deep reinforcement learning (DRL)-based EMS for HEAT. The model of HEAT is built and rotary tillage condition is defined for better conformity with the actual situation. Simulation results show that the DRL-based EMS achieves better performance.

源语言	英语
主期刊名	2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023
出版商	Institute of Electrical and Electronics Engineers Inc.
页	1095-1098
页数	4
ISBN（电子版）	9798350336030
DOI	https://doi.org/10.1109/ICMSP58539.2023.10170967
出版状态	已出版 - 2023
活动	5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023 - Chengdu, 中国期限: 19 5月 2023 → 21 5月 2023

出版系列

姓名	2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

会议

会议	5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023
国家/地区	中国
市	Chengdu
时期	19/05/23 → 21/05/23

联合国可持续发展目标

此成果有助于实现下列可持续发展目标：

访问文件

10.1109/ICMSP58539.2023.10170967

其它文件与链接

链接到 Scopus 的出版物

引用此

Liu, S., Wu, Z., Chen, X., & Li, Z. (2023). Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor. 在 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023 (页码 1095-1098). (2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMSP58539.2023.10170967

Liu, Shenyuan ; Wu, Zhiming ; Chen, Xiaokai 等. / Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor. 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 1095-1098 (2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023).

@inproceedings{7da27274993b4a3ab9e100476467a6d8,

title = "Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor",

abstract = "Interest in hybrid electric agricultural tractors (HEAT) is growing as traditional agricultural tractors consume large amounts of non-renewable resources. Energy management strategy (EMS) plays an important role in improving fuel consumption. This paper studies a deep reinforcement learning (DRL)-based EMS for HEAT. The model of HEAT is built and rotary tillage condition is defined for better conformity with the actual situation. Simulation results show that the DRL-based EMS achieves better performance.",

keywords = "Deep Deterministic Policy Gradient, Energy management strategy, hybrid electric agricultural tractors",

author = "Shenyuan Liu and Zhiming Wu and Xiaokai Chen and Zhengyu Li",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023 ; Conference date: 19-05-2023 Through 21-05-2023",

year = "2023",

doi = "10.1109/ICMSP58539.2023.10170967",

language = "English",

series = "2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1095--1098",

booktitle = "2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023",

address = "United States",

}

Liu, S, Wu, Z, Chen, X & Li, Z 2023, Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor. 在 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023. 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023, Institute of Electrical and Electronics Engineers Inc., 页码 1095-1098, 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023, Chengdu, 中国, 19/05/23. https://doi.org/10.1109/ICMSP58539.2023.10170967

Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor. / Liu, Shenyuan; Wu, Zhiming; Chen, Xiaokai 等.
2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023. Institute of Electrical and Electronics Engineers Inc., 2023. 页码 1095-1098 (2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor

AU - Liu, Shenyuan

AU - Wu, Zhiming

AU - Chen, Xiaokai

AU - Li, Zhengyu

PY - 2023

Y1 - 2023

N2 - Interest in hybrid electric agricultural tractors (HEAT) is growing as traditional agricultural tractors consume large amounts of non-renewable resources. Energy management strategy (EMS) plays an important role in improving fuel consumption. This paper studies a deep reinforcement learning (DRL)-based EMS for HEAT. The model of HEAT is built and rotary tillage condition is defined for better conformity with the actual situation. Simulation results show that the DRL-based EMS achieves better performance.

AB - Interest in hybrid electric agricultural tractors (HEAT) is growing as traditional agricultural tractors consume large amounts of non-renewable resources. Energy management strategy (EMS) plays an important role in improving fuel consumption. This paper studies a deep reinforcement learning (DRL)-based EMS for HEAT. The model of HEAT is built and rotary tillage condition is defined for better conformity with the actual situation. Simulation results show that the DRL-based EMS achieves better performance.

KW - Deep Deterministic Policy Gradient

KW - Energy management strategy

KW - hybrid electric agricultural tractors

UR - http://www.scopus.com/inward/record.url?scp=85166264066&partnerID=8YFLogxK

U2 - 10.1109/ICMSP58539.2023.10170967

DO - 10.1109/ICMSP58539.2023.10170967

M3 - Conference contribution

AN - SCOPUS:85166264066

T3 - 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

SP - 1095

EP - 1098

BT - 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023

Y2 - 19 May 2023 through 21 May 2023

ER -

Liu S, Wu Z, Chen X, Li Z. Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor. 在 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023. Institute of Electrical and Electronics Engineers Inc. 2023. 页码 1095-1098. (2023 5th International Conference on Intelligent Control, Measurement and Signal Processing, ICMSP 2023). doi: 10.1109/ICMSP58539.2023.10170967

Deep Reinforcement Learning-Based Energy Management Strategy for Hybrid Electric Agriculture Tractor

摘要

出版系列

会议

联合国可持续发展目标

访问文件

其它文件与链接

指纹

引用此