Nan Xu , Qiao Liu , Chengyin Yuan , Yongtao Yang , Yan Kong , Yifan Jia , Linhao Li , Jincheng Li
{"title":"A double-filter driving cycle generation framework with dynamometer testing and analysis","authors":"Nan Xu , Qiao Liu , Chengyin Yuan , Yongtao Yang , Yan Kong , Yifan Jia , Linhao Li , Jincheng Li","doi":"10.1016/j.trd.2026.105231","DOIUrl":null,"url":null,"abstract":"<div><div>Standard driving cycles is the basis to obtain the vehicle type-approval energy consumption. However, for users, there is a gap between the type-approval results and their experiences. For manufacturers, limited number of test cycles cause sub-optimization related issues during the vehicle design and development; for testing organizations, deterministic test cycles lead to the risk of cheating in testing. Therefore, a novel double-filter method is proposed to generate multiple stochastic driving cycles that are energy consumption comparable. Using user data as reference, chassis dynamometer tests were designed for validation of the energy consistency. Results show that the maximum deviation from the user was 2.144%, and the minimum was only 0.465%. The method proposed enables customized families of test cycles, overcoming the limitations of deterministic, finite cycles, which contribute to vehicle economy optimization and provide a dependable foundation for energy consumption estimation for city transportation systems and related policy development.</div></div>","PeriodicalId":23277,"journal":{"name":"Transportation Research Part D-transport and Environment","volume":"153 ","pages":"Article 105231"},"PeriodicalIF":8.7000,"publicationDate":"2026-04-01","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/S1361920926000246","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2026/1/20 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"ENVIRONMENTAL STUDIES","Score":null,"Total":0}
引用次数: 0
Abstract
Standard driving cycles is the basis to obtain the vehicle type-approval energy consumption. However, for users, there is a gap between the type-approval results and their experiences. For manufacturers, limited number of test cycles cause sub-optimization related issues during the vehicle design and development; for testing organizations, deterministic test cycles lead to the risk of cheating in testing. Therefore, a novel double-filter method is proposed to generate multiple stochastic driving cycles that are energy consumption comparable. Using user data as reference, chassis dynamometer tests were designed for validation of the energy consistency. Results show that the maximum deviation from the user was 2.144%, and the minimum was only 0.465%. The method proposed enables customized families of test cycles, overcoming the limitations of deterministic, finite cycles, which contribute to vehicle economy optimization and provide a dependable foundation for energy consumption estimation for city transportation systems and related policy development.
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.