Comparative Analysis of Machine Learning Models for Predicting Electric Vehicle Range

Gregorius Airlangga(1*),

(1) Universitas Katolik Indonesia Atma Jaya, Jakarta, Indonesia
(*) Corresponding Author

Abstract


This research presents a comprehensive analysis of various machine learning models to predict the electric range of electric vehicles (EVs). In the context of growing environmental concerns and the push for sustainable transportation, accurate prediction of EV range is crucial for consumer trust and wider adoption. We evaluated five different models: Linear Regression, Ridge Regression, Lasso Regression, Random Forest Regressor, and Gradient Boosting Regressor, using a dataset that included a diverse array of EV attributes. The primary evaluation metric was the Mean Squared Error (MSE), applied both in cross-validation and on a test set. Our findings revealed significant differences in performance between linear models and ensemble methods. Linear models, while computationally efficient and interpretable, showed modest predictive capabilities, likely limited by their inability to capture complex, non-linear relationships in the data. Notably, Lasso Regression exhibited the highest error rates, possibly due to its feature exclusion in regularization. In contrast, ensemble methods, particularly the Random Forest Regressor and Gradient Boosting Regressor, demonstrated superior performance, effectively modeling non-linear relationships and intricate feature interactions. This study underscores the importance of model selection in predictive tasks, highlighting that more complex models, such as ensemble methods, are often more suitable for datasets with multifaceted interactions and non-linearities. The results of this research contribute to the evolving field of electric vehicle technology, providing insights that can guide future developments in EV range prediction, a key factor in the advancement of sustainable transportation. This research aids in understanding the application of machine learning in EV range prediction and lays the groundwork for future exploration, potentially incorporating real-time data and external factors for enhanced accuracy.


Full Text:

PDF

References


L. Xin, M. Ahmad, and S. I. Khattak, “Impact of innovation in hybrid electric vehicles-related technologies on carbon dioxide emissions in the 15 most innovative countries,” Technol. Forecast. Soc. Change, vol. 196, p. 122859, 2023.

G. Bhatti, H. Mohan, and R. R. Singh, “Towards the future of smart electric vehicles: Digital twin technology,” Renew. Sustain. Energy Rev., vol. 141, p. 110801, 2021.

M. Mohammadi, J. Thornburg, and J. Mohammadi, “Towards an energy future with ubiquitous electric vehicles: Barriers and opportunities,” Energies, vol. 16, no. 17, p. 6379, 2023.

A. Ghosh, “Possibilities and challenges for the inclusion of the electric vehicle (EV) to reduce the carbon footprint in the transport sector: A review,” Energies, vol. 13, no. 10, p. 2602, 2020.

M. Muratori et al., “The rise of electric vehicles—2020 status and future expectations,” Prog. Energy, vol. 3, no. 2, p. 22002, 2021.

J. Van Mierlo et al., “Beyond the state of the art of electric vehicles: A fact-based paper of the current and prospective electric vehicle technologies,” World Electr. Veh. J., vol. 12, no. 1, p. 20, 2021.

S. C. Mukherjee and L. Ryan, “Factors influencing early battery electric vehicle adoption in Ireland,” Renew. Sustain. Energy Rev., vol. 118, p. 109504, 2020.

V. Singh, V. Singh, and S. Vaibhav, “A review and simple meta-analysis of factors influencing adoption of electric vehicles,” Transp. Res. Part D Transp. Environ., vol. 86, p. 102436, 2020.

C. Chen, G. Z. de Rubens, L. Noel, J. Kester, and B. K. Sovacool, “Assessing the socio-demographic, technical, economic and behavioral factors of Nordic electric vehicle adoption and the influence of vehicle-to-grid preferences,” Renew. Sustain. Energy Rev., vol. 121, p. 109692, 2020.

M. Kumar, K. P. Panda, R. T. Naayagi, R. Thakur, and G. Panda, “Comprehensive Review of Electric Vehicle Technology and Its Impacts: Detailed Investigation of Charging Infrastructure, Power Management, and Control Techniques,” Appl. Sci., vol. 13, no. 15, p. 8919, 2023.

P. Franzese et al., “Fast DC Charging Infrastructures for Electric Vehicles: Overview of Technologies, Standards, and Challenges,” IEEE Trans. Transp. Electrif., 2023.

I. Ullah, K. Liu, T. Yamamoto, R. E. Al Mamlook, and A. Jamal, “A comparative performance of machine learning algorithm to predict electric vehicles energy consumption: A path towards sustainability,” Energy & Environ., vol. 33, no. 8, pp. 1583–1612, 2022.

V. Chandran, C. K. Patil, A. Karthick, D. Ganeshaperumal, R. Rahim, and A. Ghosh, “State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms,” World Electr. Veh. J., vol. 12, no. 1, p. 38, 2021.

M. S. Ibrahim, W. Dong, and Q. Yang, “Machine learning driven smart electric power systems: Current trends and new perspectives,” Appl. Energy, vol. 272, p. 115237, 2020.

S. B. Vilsen and D.-I. Stroe, “Battery state-of-health modelling by multiple linear regression,” J. Clean. Prod., vol. 290, p. 125700, 2021.

K. Liu, X. Hu, H. Zhou, L. Tong, W. D. Widanage, and J. Marco, “Feature analyses and modeling of lithium-ion battery manufacturing based on random forest classification,” IEEE/ASME Trans. Mechatronics, vol. 26, no. 6, pp. 2944–2955, 2021.

A. Manoharan, K. M. Begam, V. R. Aparow, and D. Sooriamoorthy, “Artificial Neural Networks, Gradient Boosting and Support Vector Machines for electric vehicle battery state estimation: A review,” J. Energy Storage, vol. 55, p. 105384, 2022.

A. Almahdi et al., “Boosting Ensemble Learning for Freeway Crash Classification under Varying Traffic Conditions: A Hyperparameter Optimization Approach,” Sustainability, vol. 15, no. 22, p. 15896, 2023.

W. Zhang, S. Wang, L. Wan, Z. Zhang, and D. Zhao, “Information perspective for understanding consumers’ perceptions of electric vehicles and adoption intentions,” Transp. Res. Part D Transp. Environ., vol. 102, p. 103157, 2022.

S. E. Bibri, J. Krogstie, A. Kaboli, and A. Alahi, “Smarter eco-cities and their leading-edge artificial intelligence of things solutions for environmental sustainability: A comprehensive systematic review,” Environ. Sci. Ecotechnology, vol. 19, p. 100330, 2024.

N. V Martyushev, B. V Malozyomov, S. N. Sorokova, E. A. Efremenkov, D. V Valuev, and M. Qi, “Review models and methods for determining and predicting the reliability of technical systems and transport,” Mathematics, vol. 11, no. 15, p. 3317, 2023.

M.-F. Ng, J. Zhao, Q. Yan, G. J. Conduit, and Z. W. Seh, “Predicting the state of charge and health of batteries using data-driven machine learning,” Nat. Mach. Intell., vol. 2, no. 3, pp. 161–170, 2020.

J. Zhao et al., “Battery safety: Machine learning-based prognostics,” Prog. Energy Combust. Sci., vol. 102, p. 101142, 2024.

M. Sharif and H. Seker, “Smart EV Charging with Context-Awareness: Enhancing Resource Utilization via Deep Reinforcement Learning,” IEEE Access, 2024.

D. A. Ramadhan, S. Rochimah, and U. L. Yuhana, “Classification of non-functional requirements using Semantic-FSKNN based ISO/IEC 9126,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 13, no. 4, pp. 1456–1465, 2015.

Y. Singhal, “Electric Vehicle Population Dataset.” 2022.




DOI: http://dx.doi.org/10.30645/jurasik.v9i1.740

DOI (PDF): http://dx.doi.org/10.30645/jurasik.v9i1.740.g715

Refbacks

  • There are currently no refbacks.



JURASIK (Jurnal Riset Sistem Informasi dan Teknik Informatika)
Published Papers Indexed/Abstracted By:

Jumlah Kunjungan : View My Stats