first_pagesettingsOrder Article Reprints Open AccessArticle Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database by Maksymilian Mądziel 1,*ORCID andTiziana Campisi 2,*ORCID 1 Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland 2 Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy * Authors to whom correspondence should be addressed. Energies 2023, 16(3), 1437; https://doi.org/10.3390/en16031437 Received: 9 January 2023 / Revised: 22 January 2023 / Accepted: 30 January 2023 / Published: 1 February 2023 (This article belongs to the Special Issue Digital Services, Design and Cost Implications for Electric Vehicles Data Based Services and Fleet Management Systems) Download Browse Figures Versions Notes Abstract Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.
Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database
Campisi, Tiziana
Writing – Review & Editing
2023-01-01
Abstract
first_pagesettingsOrder Article Reprints Open AccessArticle Energy Consumption of Electric Vehicles: Analysis of Selected Parameters Based on Created Database by Maksymilian Mądziel 1,*ORCID andTiziana Campisi 2,*ORCID 1 Faculty of Mechanical Engineering and Aeronautics, Rzeszow University of Technology, 35-959 Rzeszow, Poland 2 Faculty of Engineering and Architecture, Kore University of Enna, Cittadella Universitaria, 94100 Enna, Italy * Authors to whom correspondence should be addressed. Energies 2023, 16(3), 1437; https://doi.org/10.3390/en16031437 Received: 9 January 2023 / Revised: 22 January 2023 / Accepted: 30 January 2023 / Published: 1 February 2023 (This article belongs to the Special Issue Digital Services, Design and Cost Implications for Electric Vehicles Data Based Services and Fleet Management Systems) Download Browse Figures Versions Notes Abstract Electric vehicles in a short time will make up the majority of the fleet of vehicles used in general. This state of affairs will generate huge sets of data, which can be further investigated. The paper presents a methodology for the analysis of electric vehicle data, with particular emphasis on the energy consumption parameter. The prepared database contains data for 123 electric vehicles for analysis. Data analysis was carried out in a Python environment with the use of the dabl API library. Presentation of the results was made on the basis of data classification for continuous and categorical features vs. target parameters. Additionally, a heatmap Pearson correlation coefficient was performed to correlate the energy consumption parameter with the other parameters studied. Through the data classification for the studied dataset, it can be concluded that there is no correlation against energy consumption for the parameter charging speed; in contrast, for the parameters range and maximum velocity, a positive correlation can be observed. The negative correlation with the parameter energy consumption is for the parameter acceleration to 100 km/h. The methodology presented to assess data from electric vehicles can be scalable for another dataset to prepare data for creating machine learning models, for example.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
