The evolution of electric vehicles has emerged among the possible strategies towards achieving energy security. The amount of data produced is growing very fast, providing opportunities for information discovery through big data analysis. This study undertakes a comprehensive data analysis of electric vehicles produced from 1997 to 2024, exploring the development trends on data evaluation system that considers electric vehicle models, types (Battery Electric Vehicles - BEV, Plug-in Hybrid Electric Vehicles - PHEV, Clean Alternative Fuel Vehicle - CAFV Eligibility), electric vehicle range, and base Manufacturer Suggested Retail Price. Data analysis employs k-means as an unsupervised machine learning algorithm for dataset partitioning into clusters. Factor analysis and Principal Component Analysis (PCA) were also employed as supervised learning methods to explore patterns in the dataset without specific emphasis on underlying factors while retaining maximum variance. Further visualizations were carried out using scatterplots, correlation matrices, contingency tables, density plots, and box plots. This study was able to uncover dynamic directions and future industry trends in addressing significant challenges in sustainable development, the study recommends the use of datasets with increased observations spanning the period from 2020 to 2024 with emphasis on electric vehicle prices and their electric ranges. These are essential factors for a comprehensive understanding of the electric vehicle market.
| Published in | Control Science and Engineering (Volume 8, Issue 1) |
| DOI | 10.11648/j.cse.20240801.11 |
| Page(s) | 1-12 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Electric Vehicles, Energy Security, Sustainable Development, Data Analysis, Industry Trends
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APA Style
Oyeshola, J. A., Namadi, M. M., Afolabi, S., Jimoh, T. O. (2024). Towards Achieving Energy Security: Data-Driven Analysis of Electric Vehicle Trends (1997-2024). Control Science and Engineering, 8(1), 1-12. https://doi.org/10.11648/j.cse.20240801.11
ACS Style
Oyeshola, J. A.; Namadi, M. M.; Afolabi, S.; Jimoh, T. O. Towards Achieving Energy Security: Data-Driven Analysis of Electric Vehicle Trends (1997-2024). Control Sci. Eng. 2024, 8(1), 1-12. doi: 10.11648/j.cse.20240801.11
AMA Style
Oyeshola JA, Namadi MM, Afolabi S, Jimoh TO. Towards Achieving Energy Security: Data-Driven Analysis of Electric Vehicle Trends (1997-2024). Control Sci Eng. 2024;8(1):1-12. doi: 10.11648/j.cse.20240801.11
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Download@article{10.11648/j.cse.20240801.11,
author = {Jimoh Afeez Oyeshola and Muhammad Muktar Namadi and Sulaiman Afolabi and Teslim Oyewale Jimoh},
title = {Towards Achieving Energy Security: Data-Driven Analysis of Electric Vehicle Trends (1997-2024)},
journal = {Control Science and Engineering},
volume = {8},
number = {1},
pages = {1-12},
doi = {10.11648/j.cse.20240801.11},
url = {https://doi.org/10.11648/j.cse.20240801.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20240801.11},
abstract = {The evolution of electric vehicles has emerged among the possible strategies towards achieving energy security. The amount of data produced is growing very fast, providing opportunities for information discovery through big data analysis. This study undertakes a comprehensive data analysis of electric vehicles produced from 1997 to 2024, exploring the development trends on data evaluation system that considers electric vehicle models, types (Battery Electric Vehicles - BEV, Plug-in Hybrid Electric Vehicles - PHEV, Clean Alternative Fuel Vehicle - CAFV Eligibility), electric vehicle range, and base Manufacturer Suggested Retail Price. Data analysis employs k-means as an unsupervised machine learning algorithm for dataset partitioning into clusters. Factor analysis and Principal Component Analysis (PCA) were also employed as supervised learning methods to explore patterns in the dataset without specific emphasis on underlying factors while retaining maximum variance. Further visualizations were carried out using scatterplots, correlation matrices, contingency tables, density plots, and box plots. This study was able to uncover dynamic directions and future industry trends in addressing significant challenges in sustainable development, the study recommends the use of datasets with increased observations spanning the period from 2020 to 2024 with emphasis on electric vehicle prices and their electric ranges. These are essential factors for a comprehensive understanding of the electric vehicle market.
},
year = {2024}
}
TY - JOUR T1 - Towards Achieving Energy Security: Data-Driven Analysis of Electric Vehicle Trends (1997-2024) AU - Jimoh Afeez Oyeshola AU - Muhammad Muktar Namadi AU - Sulaiman Afolabi AU - Teslim Oyewale Jimoh Y1 - 2024/01/11 PY - 2024 N1 - https://doi.org/10.11648/j.cse.20240801.11 DO - 10.11648/j.cse.20240801.11 T2 - Control Science and Engineering JF - Control Science and Engineering JO - Control Science and Engineering SP - 1 EP - 12 PB - Science Publishing Group SN - 2994-7421 UR - https://doi.org/10.11648/j.cse.20240801.11 AB - The evolution of electric vehicles has emerged among the possible strategies towards achieving energy security. The amount of data produced is growing very fast, providing opportunities for information discovery through big data analysis. This study undertakes a comprehensive data analysis of electric vehicles produced from 1997 to 2024, exploring the development trends on data evaluation system that considers electric vehicle models, types (Battery Electric Vehicles - BEV, Plug-in Hybrid Electric Vehicles - PHEV, Clean Alternative Fuel Vehicle - CAFV Eligibility), electric vehicle range, and base Manufacturer Suggested Retail Price. Data analysis employs k-means as an unsupervised machine learning algorithm for dataset partitioning into clusters. Factor analysis and Principal Component Analysis (PCA) were also employed as supervised learning methods to explore patterns in the dataset without specific emphasis on underlying factors while retaining maximum variance. Further visualizations were carried out using scatterplots, correlation matrices, contingency tables, density plots, and box plots. This study was able to uncover dynamic directions and future industry trends in addressing significant challenges in sustainable development, the study recommends the use of datasets with increased observations spanning the period from 2020 to 2024 with emphasis on electric vehicle prices and their electric ranges. These are essential factors for a comprehensive understanding of the electric vehicle market. VL - 8 IS - 1 ER -
Department of Informatics, University of Louisiana, Lafayette, USA
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