A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential
Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and chargi...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10026264/ |
| _version_ | 1797938190583070720 |
|---|---|
| author | Alpaslan Demirci Said Mirza Tercan Umit Cali Ismail Nakir |
| author_facet | Alpaslan Demirci Said Mirza Tercan Umit Cali Ismail Nakir |
| author_sort | Alpaslan Demirci |
| collection | DOAJ |
| description | Electric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than ten minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects. |
| first_indexed | 2024-04-10T18:55:51Z |
| format | Article |
| id | doaj.art-811996b50ca947a089e623dc7dfffe07 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-10T18:55:51Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-811996b50ca947a089e623dc7dfffe072023-02-01T00:00:37ZengIEEEIEEE Access2169-35362023-01-01119149916510.1109/ACCESS.2023.324010210026264A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid PotentialAlpaslan Demirci0https://orcid.org/0000-0002-1038-7224Said Mirza Tercan1https://orcid.org/0000-0003-1663-713XUmit Cali2https://orcid.org/0000-0002-6402-0479Ismail Nakir3https://orcid.org/0000-0002-7051-1733Department of Electrical Engineering, Yildiz Technical University, Istanbul, TurkeyDepartment of Electrical Engineering, Yildiz Technical University, Istanbul, TurkeyDepartment of Electric Power Engineering, Norwegian University of Science and Technology, Trondheim, NorwayDepartment of Electrical Engineering, Yildiz Technical University, Istanbul, TurkeyElectric vehicles (EVs) improve the power grid by increasing intermittent renewable energy consumption and providing financial support to EV users via vehicle-to-grid (V2G) integration. While estimating these advantages, a number of studies have neglected to consider the effect of driving and charging behavior patterns on their results. This article provides a framework that systematically evaluates EV driving and charging behaviors to improve charge management in the light of recent standards and advancements. In addition, the collected data on driving habits are analyzed in order to provide a consistent and usable dataset. By evaluating the individual and simultaneous charging demand characteristics, the V2G potential is further explored. Moreover, managerial recommendations for EV charging management are offered by improving the time step using the Bootstrap approach for more precise results than lower resolution. It is also addressed that the simultaneous use of a limited number of EVs required minimum time. According to the findings of this study, daily travel habits have a crucial influence in defining seasonal and individual charging demands. In order to continue with EV charging-related assessments with a confidence interval of more than 95%, the findings suggest that time steps of lower than ten minutes must be used. In addition, the purpose of this study is to assist researchers from academia and business with further information as they build initiatives linked to EV charging infrastructure and real-time charging management standards that account environmental aspects.https://ieeexplore.ieee.org/document/10026264/Bootstrapcharging behaviordistributed networkdriving dataelectric vehicle |
| spellingShingle | Alpaslan Demirci Said Mirza Tercan Umit Cali Ismail Nakir A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential IEEE Access Bootstrap charging behavior distributed network driving data electric vehicle |
| title | A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential |
| title_full | A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential |
| title_fullStr | A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential |
| title_full_unstemmed | A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential |
| title_short | A Comprehensive Data Analysis of Electric Vehicle User Behaviors Toward Unlocking Vehicle-to-Grid Potential |
| title_sort | comprehensive data analysis of electric vehicle user behaviors toward unlocking vehicle to grid potential |
| topic | Bootstrap charging behavior distributed network driving data electric vehicle |
| url | https://ieeexplore.ieee.org/document/10026264/ |
| work_keys_str_mv | AT alpaslandemirci acomprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential AT saidmirzatercan acomprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential AT umitcali acomprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential AT ismailnakir acomprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential AT alpaslandemirci comprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential AT saidmirzatercan comprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential AT umitcali comprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential AT ismailnakir comprehensivedataanalysisofelectricvehicleuserbehaviorstowardunlockingvehicletogridpotential |