A Comparative Machine Learning Approach for Energy Consumption Prediction in Hybrid Vehicles
DOI:
https://doi.org/10.38032/scse.2026.4.283Keywords:
Hybrid Vehicle, Fuel Consumption, NHTS, Machine LearningAbstract
With the rise in environmental concerns and global shift toward sustainable mobility, accurately predicting energy consumption in hybrid vehicles (HVs) is essential for reliable transportation planning and optimizing efficient energy management. This study employs the National Household Travel Survey (NHTS) dataset to develop predictive models for estimating energy usage in HVs. In this paper, four machine learning models (XGBoost, Linear Regression, Random Forest, and Neural Network) are implemented to analyze and compare their effectiveness in forecasting energy consumption. The dataset is preprocessed and standardized, followed by model training and 5-fold cross-validation. The performance of each model is determined using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the Coefficient of Determination (R²). Among them, the Random Forest model achieved the best effectiveness with lower RMSE and MAE along with the highest R2 score for predicting energy consumption in hybrid vehicles. The findings show the prospective of machine learning techniques, particularly the Random Forest model, which delivers the highest accuracy in predicting hybrid vehicle energy consumption. This result supports more informed decision-making for developing eco-friendly transport solutions, reducing fuel consumption and emissions.
Downloads
Downloads
Downloads
References
[1] H.-J. Althaus, “Modern individual mobility,” Int J Life Cycle Assess, vol. 17, no. 3, pp. 267–269, 2012. DOI: https://doi.org/10.1007/s11367-011-0365-8
[2] A. Emadi, Y. J. Lee, and K. Rajashekara, “Power electronics and motor drives in electric, hybrid electric, and plug-in hybrid electric vehicles,” IEEE Transactions on industrial electronics, vol. 55, no. 6, pp. 2237–2245, 2008. DOI: https://doi.org/10.1109/TIE.2008.922768
[3] J. Zhang, Z. Wang, P. Liu, and Z. Zhang, “Energy consumption analysis and prediction of electric vehicles based on real-world driving data,” Appl Energy, vol. 275, p. 115408, 2020. DOI: https://doi.org/10.1016/j.apenergy.2020.115408
[4] M. A. Nobi, “A Review: Machine Learning with Electric Vehicles Applications,” Available at SSRN 5085327, 2024. DOI: https://doi.org/10.2139/ssrn.5085327
[5] Y. Yao et al., “Vehicle fuel consumption prediction method based on driving behavior data collected from smartphones,” J Adv Transp, vol. 2020, no. 1, p. 9263605, 2020. DOI: https://doi.org/10.1155/2020/9263605
[6] J.-Y. Kim and S.-B. Cho, “Electric energy consumption prediction by deep learning with state explainable autoencoder,” Energies (Basel), vol. 12, no. 4, p. 739, 2019. DOI: https://doi.org/10.3390/en12040739
[7] 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 & Environment, vol. 33, no. 8, pp. 1583–1612, Dec. 2022, doi: 10.1177/0958305X211044998. DOI: https://doi.org/10.1177/0958305X211044998
[8] A. Prashar, “Adopting PDCA (Plan-Do-Check-Act) cycle for energy optimization in energy-intensive SMEs,” J Clean Prod, vol. 145, pp. 277–293, 2017. DOI: https://doi.org/10.1016/j.jclepro.2017.01.068
[9] B. B. Ekici and U. T. Aksoy, “Prediction of building energy consumption by using artificial neural networks,” Advances in Engineering Software, vol. 40, no. 5, pp. 356–362, 2009. DOI: https://doi.org/10.1016/j.advengsoft.2008.05.003
[10] M. Mądziel, “Impact of Weather Conditions on Energy Consumption Modeling for Electric Vehicles,” Energies (Basel), vol. 18, no. 8, p. 1994, 2025. DOI: https://doi.org/10.3390/en18081994
[11] M. Spichakova, J. Belikov, K. Nõu, and E. Petlenkov, “Feature engineering for short-term forecast of energy consumption,” in 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), IEEE, 2019, pp. 1–5. DOI: https://doi.org/10.1109/ISGTEurope.2019.8905698
[12] N. Shrestha, “Detecting multicollinearity in regression analysis,” Am J Appl Math Stat, vol. 8, no. 2, pp. 39–42, 2020. DOI: https://doi.org/10.12691/ajams-8-2-1
[13] A. F. Schmidt and C. Finan, “Linear regression and the normality assumption,” J Clin Epidemiol, vol. 98, pp. 146–151, 2018. DOI: https://doi.org/10.1016/j.jclinepi.2017.12.006
[14] L. Breiman, “Random forests,” Mach Learn, vol. 45, no. 1, pp. 5–32, 2001. DOI: https://doi.org/10.1023/A:1010933404324
[15] A. H. Emtiaj, R. H. Rafi, M. A. Nayeem, and M. S. Rahman, “CRISMER: A transformer-based Interpretable Deep Learning Approach for Genome-wide CRISPR Cas-9 Off-Target Prediction and Optimization,” bioRxiv, p. 2025, 2025. DOI: https://doi.org/10.1101/2025.05.03.652008
[16] W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” Bull Math Biophys, vol. 5, no. 4, pp. 115–133, 1943. DOI: https://doi.org/10.1007/BF02478259
[17] J. Ziółkowski, M. Oszczypała, J. Małachowski, and J. Szkutnik-Rogoż, “Use of artificial neural networks to predict fuel consumption on the basis of technical parameters of vehicles,” Energies (Basel), vol. 14, no. 9, p. 2639, 2021. DOI: https://doi.org/10.3390/en14092639
[18] J. Fan et al., “Comparison of Support Vector Machine and Extreme Gradient Boosting for predicting daily global solar radiation using temperature and precipitation in humid subtropical climates: A case study in China,” Energy Convers Manag, vol. 164, pp. 102–111, 2018. DOI: https://doi.org/10.1016/j.enconman.2018.02.087
Published
Conference Proceedings Volume
Section
License
Copyright (c) 2026 Bishawajit Chakraborty Chakraborty , Arpita Paul Paul , Bhubon Thiotonius Costa Costa , MD. Shajratul Alam Towhid Towhid (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All the articles published by this journal are licensed under a Creative Commons Attribution 4.0 International License
How to Cite
