N. Zacharof, S. Doulgeris, A. Zafeiriadis, A. Dimaratos, René van Gijlswijk, Sonsoles Díaz, Zissis Samaras
{"title":"A simulation model of the real-world fuel and energy consumption of light-duty vehicles","authors":"N. Zacharof, S. Doulgeris, A. Zafeiriadis, A. Dimaratos, René van Gijlswijk, Sonsoles Díaz, Zissis Samaras","doi":"10.3389/ffutr.2024.1334651","DOIUrl":null,"url":null,"abstract":"The European Union has intensified efforts to reduce CO2 emissions from the transport sector, with the target of reducing tailpipe CO2 emissions from light-duty vehicle new registrations by 55% by 2030 and achieving zero emissions by 2035 according to the “Fit for 55” package. To promote fuel and energy consumption awareness among users under real-world conditions the MILE21—LIFE project provided tools such as a self-reporting tool and a find-a-car tool that included the official and representative on-road fuel/energy consumption values. In order to produce representative values, an in-house vehicle longitudinal dynamics simulation model was developed for use in the background of the on-line platform utilizing only a limited amount of inputs. To achieve this, the applied methodology is based on precalculated efficiency values. These values have been produced using vehicle micro-model simulations covering a wide range of operating conditions. The model was validated using measurements from a dedicated testing campaign and performed well for petrol vehicles with an average divergence of −1.1%. However, the model showed a divergence of 9.7% for diesel vehicles, 10.6% for hybrids and 8.7% for plug-in hybrids. The model was also applied to US vehicles and showed a divergence of 1.2% and 10% for city and highway driving, respectively. The application of the developed model presented in this work showed that it is possible to predict real-world fuel and energy consumption with the desired accuracy using a simplified approach with limited input data.","PeriodicalId":491609,"journal":{"name":"Frontiers in future transportation","volume":"6 12","pages":""},"PeriodicalIF":1.9000,"publicationDate":"2024-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Frontiers in future transportation","FirstCategoryId":"0","ListUrlMain":"https://doi.org/10.3389/ffutr.2024.1334651","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The European Union has intensified efforts to reduce CO2 emissions from the transport sector, with the target of reducing tailpipe CO2 emissions from light-duty vehicle new registrations by 55% by 2030 and achieving zero emissions by 2035 according to the “Fit for 55” package. To promote fuel and energy consumption awareness among users under real-world conditions the MILE21—LIFE project provided tools such as a self-reporting tool and a find-a-car tool that included the official and representative on-road fuel/energy consumption values. In order to produce representative values, an in-house vehicle longitudinal dynamics simulation model was developed for use in the background of the on-line platform utilizing only a limited amount of inputs. To achieve this, the applied methodology is based on precalculated efficiency values. These values have been produced using vehicle micro-model simulations covering a wide range of operating conditions. The model was validated using measurements from a dedicated testing campaign and performed well for petrol vehicles with an average divergence of −1.1%. However, the model showed a divergence of 9.7% for diesel vehicles, 10.6% for hybrids and 8.7% for plug-in hybrids. The model was also applied to US vehicles and showed a divergence of 1.2% and 10% for city and highway driving, respectively. The application of the developed model presented in this work showed that it is possible to predict real-world fuel and energy consumption with the desired accuracy using a simplified approach with limited input data.