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 , Ke Liang , Wei Guan , Hailang Sang , Shengkai Zhou , Lei Deng , 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 , Ke Liang , Wei Guan , Hailang Sang , Shengkai Zhou , Lei Deng , 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}
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.
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.