基于油耗预测的柴油车能量管理策略

IF 7.7 1区 工程技术 Q1 ENVIRONMENTAL STUDIES
Jiahao Zhao , Ke Liang , Wei Guan , Hailang Sang , Shengkai Zhou , Lei Deng , Mingzhang Pan
{"title":"基于油耗预测的柴油车能量管理策略","authors":"Jiahao Zhao ,&nbsp;Ke Liang ,&nbsp;Wei Guan ,&nbsp;Hailang Sang ,&nbsp;Shengkai Zhou ,&nbsp;Lei Deng ,&nbsp;Mingzhang Pan","doi":"10.1016/j.trd.2025.104896","DOIUrl":null,"url":null,"abstract":"<div><div>Although hybrid electric and pure electric vehicles are developing rapidly, diesel vehicles also remain their dominant position in some fields such as transportation. For this reason, it is indispensable for diesel vehicle to reduce energy waste by an effective energy management strategy (EMS), with accurate fuel consumption prediction as its foundation. In this study, a hybrid long short-term memory model optimized by grey wolf optimization (GWO-LSTM) and an EMS of diesel vehicles based on model predictive control combined with GWO-LSTM (GL-MPC) are proposed for predicting and controlling fuel consumption in real-world diesel vehicles. The results of contrast experiment indicate mean squared error (MSE) of the proposed GWO-LSTM model can achieve 0.0141. What’s more, the results of tracking effectiveness analysis indicate the proposed GL-MPC model can achieve stable tracking of the reference trajectory after 0.3 s, which prove it can control fuel consumption in a predetermined value.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"146 ","pages":"Article 104896"},"PeriodicalIF":7.7000,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Energy management strategy for diesel vehicles based on fuel consumption prediction\",\"authors\":\"Jiahao Zhao ,&nbsp;Ke Liang ,&nbsp;Wei Guan ,&nbsp;Hailang Sang ,&nbsp;Shengkai Zhou ,&nbsp;Lei Deng ,&nbsp;Mingzhang Pan\",\"doi\":\"10.1016/j.trd.2025.104896\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Although hybrid electric and pure electric vehicles are developing rapidly, diesel vehicles also remain their dominant position in some fields such as transportation. For this reason, it is indispensable for diesel vehicle to reduce energy waste by an effective energy management strategy (EMS), with accurate fuel consumption prediction as its foundation. In this study, a hybrid long short-term memory model optimized by grey wolf optimization (GWO-LSTM) and an EMS of diesel vehicles based on model predictive control combined with GWO-LSTM (GL-MPC) are proposed for predicting and controlling fuel consumption in real-world diesel vehicles. The results of contrast experiment indicate mean squared error (MSE) of the proposed GWO-LSTM model can achieve 0.0141. What’s more, the results of tracking effectiveness analysis indicate the proposed GL-MPC model can achieve stable tracking of the reference trajectory after 0.3 s, which prove it can control fuel consumption in a predetermined value.</div></div>\",\"PeriodicalId\":23277,\"journal\":{\"name\":\"Transportation Research Part D-transport and Environment\",\"volume\":\"146 \",\"pages\":\"Article 104896\"},\"PeriodicalIF\":7.7000,\"publicationDate\":\"2025-06-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Transportation Research Part D-transport and Environment\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1361920925003062\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL STUDIES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Transportation Research Part D-transport and Environment","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1361920925003062","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0

摘要

虽然混合动力汽车和纯电动汽车发展迅速,但在交通运输等一些领域,柴油车仍然占据主导地位。因此,柴油车必须以准确的油耗预测为基础,通过有效的能源管理策略(EMS)来减少能源浪费。本研究提出了一种基于灰狼优化的混合长短期记忆模型(GWO-LSTM)和一种基于模型预测控制与GWO-LSTM (GL-MPC)相结合的柴油车EMS,用于预测和控制实际柴油车的油耗。对比实验结果表明,所提出的GWO-LSTM模型的均方误差(MSE)可达到0.0141。跟踪效果分析结果表明,所提出的GL-MPC模型能够在0.3 s后实现对参考轨迹的稳定跟踪,证明该模型能够将油耗控制在预定值内。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Energy management strategy for diesel vehicles based on fuel consumption prediction
Although hybrid electric and pure electric vehicles are developing rapidly, diesel vehicles also remain their dominant position in some fields such as transportation. For this reason, it is indispensable for diesel vehicle to reduce energy waste by an effective energy management strategy (EMS), with accurate fuel consumption prediction as its foundation. In this study, a hybrid long short-term memory model optimized by grey wolf optimization (GWO-LSTM) and an EMS of diesel vehicles based on model predictive control combined with GWO-LSTM (GL-MPC) are proposed for predicting and controlling fuel consumption in real-world diesel vehicles. The results of contrast experiment indicate mean squared error (MSE) of the proposed GWO-LSTM model can achieve 0.0141. What’s more, the results of tracking effectiveness analysis indicate the proposed GL-MPC model can achieve stable tracking of the reference trajectory after 0.3 s, which prove it can control fuel consumption in a predetermined value.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
14.40
自引率
9.20%
发文量
314
审稿时长
39 days
期刊介绍: Transportation Research Part D: Transport and Environment focuses on original research exploring the environmental impacts of transportation, policy responses to these impacts, and their implications for transportation system design, planning, and management. The journal comprehensively covers the interaction between transportation and the environment, ranging from local effects on specific geographical areas to global implications such as natural resource depletion and atmospheric pollution. We welcome research papers across all transportation modes, including maritime, air, and land transportation, assessing their environmental impacts broadly. Papers addressing both mobile aspects and transportation infrastructure are considered. The journal prioritizes empirical findings and policy responses of regulatory, planning, technical, or fiscal nature. Articles are policy-driven, accessible, and applicable to readers from diverse disciplines, emphasizing relevance and practicality. We encourage interdisciplinary submissions and welcome contributions from economically developing and advanced countries alike, reflecting our international orientation.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信