Enhancing Electric Vehicle Range Prediction through Deep Learning: An Autoencoder and Neural Network Approach

Gregorius Airlangga


The burgeoning adoption of electric vehicles (EVs) signifies a pivotal shift towards sustainable transportation, necessitated by the global imperative to mitigate climate change impacts. Central to this transition is the resolution of range anxiety, a significant barrier impeding wider EV acceptance. This research introduces a novel deep learning framework combining autoencoders and deep neural networks (DNNs) to predict EV range more accurately and reliably. Leveraging a comprehensive dataset from the "Electric Vehicle Population Data," we embarked on a meticulous process of data cleaning, feature engineering, and preprocessing to prepare the dataset for analysis. The study innovatively applies an autoencoder for unsupervised feature learning, effectively reducing dimensionality and extracting salient features from high-dimensional EV data. Subsequently, a DNN model utilizes these features to predict the EV range, offering insights into the vehicle's performance across various conditions. Employing a 10-fold cross-validation approach, the model's efficacy is rigorously evaluated, ensuring robustness and generalizability of the predictions. Our methodology demonstrates a significant enhancement in prediction accuracy compared to conventional machine learning models, as evidenced by the Mean Squared Error (MSE) metric. This research not only contributes to the academic discourse on sustainable transportation and deep learning applications but also provides practical insights for manufacturers, policymakers, and consumers aiming to navigate the complexities of EV adoption and infrastructure development. By addressing the critical challenge of range prediction, this study paves the way for advancing EV analytics, ultimately supporting the transition to a more sustainable and efficient transportation ecosystem.


Autoencoder; Comparison; Deep Learning; Electric Vehicle Range; Neural Network

Full Text:



S. Agostinelli and others, “Optimization and management of microgrids in the built environment based on intelligent digital twins,” 2024.

Z. A. Mani and K. Goniewicz, “Adapting disaster preparedness strategies to changing climate patterns in Saudi Arabia: A rapid review,” Sustainability, vol. 15, no. 19, p. 14279, 2023.

A. Adel, “Unlocking the Future: Fostering Human--Machine Collaboration and Driving Intelligent Automation through Industry 5.0 in Smart Cities,” Smart Cities, vol. 6, no. 5, pp. 2742–2782, 2023.

I. Aijaz and A. Ahmad, “Electric vehicles for environmental sustainability,” Smart Technol. Energy Environ. Sustain., pp. 131–145, 2022.

A. Alimujiang and P. Jiang, “Synergy and co-benefits of reducing CO2 and air pollutant emissions by promoting electric vehicles—A case of Shanghai,” Energy Sustain. Dev., vol. 55, pp. 181–189, 2020.

J. L. Breuer, R. C. Samsun, D. Stolten, and R. Peters, “How to reduce the greenhouse gas emissions and air pollution caused by light and heavy duty vehicles with battery-electric, fuel cell-electric and catenary trucks,” Environ. Int., vol. 152, p. 106474, 2021.

G. Vishnu, D. Kaliyaperumal, R. Jayaprakash, A. Karthick, V. Kumar Chinnaiyan, and A. Ghosh, “Review of Challenges and Opportunities in the Integration of Electric Vehicles to the Grid,” World Electr. Veh. J., vol. 14, no. 9, p. 259, 2023.

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.

T. Capuder, D. M. Sprčić, D. Zoričić, and H. Pandžić, “Review of challenges and assessment of electric vehicles integration policy goals: Integrated risk analysis approach,” Int. J. Electr. Power & Energy Syst., vol. 119, p. 105894, 2020.

G. F. Savari et al., “Assessment of charging technologies, infrastructure and charging station recommendation schemes of electric vehicles: A review,” Ain Shams Eng. J., vol. 14, no. 4, p. 101938, 2023.

M. Straka et al., “Predicting popularity of electric vehicle charging infrastructure in urban context,” IEEE Access, vol. 8, pp. 11315–11327, 2020.

S. Shahriar, A.-R. Al-Ali, A. H. Osman, S. Dhou, and M. Nijim, “Machine learning approaches for EV charging behavior: A review,” IEEE Access, vol. 8, pp. 168980–168993, 2020.

H. Alqahtani and G. Kumar, “Machine learning for enhancing transportation security: A comprehensive analysis of electric and flying vehicle systems,” Eng. Appl. Artif. Intell., vol. 129, p. 107667, 2024.

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.

C. B. Head, P. Jasper, M. McConnachie, L. Raftree, and G. Higdon, “Large language model applications for evaluation: Opportunities and ethical implications,” New Dir. Eval., vol. 2023, no. 178–179, pp. 33–46, 2023.

R. R. Kumar and K. Alok, “Adoption of electric vehicle: A literature review and prospects for sustainability,” J. Clean. Prod., vol. 253, p. 119911, 2020.

O. Frendo, J. Graf, N. Gaertner, and H. Stuckenschmidt, “Data-driven smart charging for heterogeneous electric vehicle fleets,” Energy AI, vol. 1, p. 100007, 2020.

L. N. Mahiban and M. Emimal, “Longevity of Electric Vehicle Operations,” Qeios, 2023.

Y. Wang, E. Yao, and L. Pan, “Electric vehicle drivers’ charging behavior analysis considering heterogeneity and satisfaction,” J. Clean. Prod., vol. 286, p. 124982, 2021.

B. Foley, K. Degirmenci, and T. Yigitcanlar, “Factors affecting electric vehicle uptake: Insights from a descriptive analysis in Australia,” Urban Sci., vol. 4, no. 4, p. 57, 2020.

Q. Xing, Z. Chen, Z. Zhang, R. Wang, and T. Zhang, “Modelling driving and charging behaviours of electric vehicles using a data-driven approach combined with behavioural economics theory,” J. Clean. Prod., vol. 324, p. 129243, 2021.

H. Rauf, M. Khalid, and N. Arshad, “Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling,” Renew. Sustain. Energy Rev., vol. 156, p. 111903, 2022.

V. K. Ramachandaramurthy, A. M. Ajmal, P. Kasinathan, K. M. Tan, J. Y. Yong, and R. Vinoth, “Social Acceptance and Preference of EV Users: A Review,” IEEE Access, 2023.

M. R. Wahid, B. A. Budiman, E. Joelianto, and M. Aziz, “A review on drive train technologies for passenger electric vehicles,” Energies, vol. 14, no. 20, p. 6742, 2021.

J. Zhao et al., “Battery prognostics and health management from a machine learning perspective,” J. Power Sources, vol. 581, p. 233474, 2023.

K. Choudhary et al., “Recent advances and applications of deep learning methods in materials science,” npj Comput. Mater., vol. 8, no. 1, p. 59, 2022.

M.-H. Lee, “Identifying correlation between the open-circuit voltage and the frontier orbital energies of non-fullerene organic solar cells based on interpretable machine-learning approaches,” Sol. Energy, vol. 234, pp. 360–367, 2022.

R. Ochoa-Barragán, J. M. Ponce-Ortega, and J. Tovar-Facio, “Long-term energy transition planning: Integrating battery system degradation and replacement for sustainable power systems,” Sustain. Prod. Consum., vol. 42, pp. 335–350, 2023.

R. K. R. Karduri, “Integrating Renewable Energy into Existing Power Systems: Challenges and Opportunities,” Int. J. Adv. Res. Manag. Archit. Technol. & Eng. (IJARMATE)(Mar 2018).

P. Mishra and G. Singh, “Energy management systems in sustainable smart cities based on the internet of energy: A technical review,” Energies, vol. 16, no. 19, p. 6903, 2023.

S. S. Ali and B. J. Choi, “State-of-the-art artificial intelligence techniques for distributed smart grids: A review,” Electronics, vol. 9, no. 6, p. 1030, 2020.

M. S. H. Lipu et al., “Artificial Intelligence Approaches for Advanced Battery Management System in Electric Vehicle Applications: A Statistical Analysis towards Future Research Opportunities,” Vehicles, vol. 6, no. 1, pp. 22–70, 2023.

C. Vidal, P. Malysz, P. Kollmeyer, and A. Emadi, “Machine learning applied to electrified vehicle battery state of charge and state of health estimation: State-of-the-art,” Ieee Access, vol. 8, pp. 52796–52814, 2020.

C. S. Wickramasinghe, D. L. Marino, and M. Manic, “ResNet autoencoders for unsupervised feature learning from high-dimensional data: Deep models resistant to performance degradation,” IEEE Access, vol. 9, pp. 40511–40520, 2021.

W. H. L. Pinaya, S. Vieira, R. Garcia-Dias, and A. Mechelli, “Autoencoders,” in Machine learning, Elsevier, 2020, pp. 193–208.

S. Chen and W. Guo, “Auto-Encoders in Deep Learning—A Review with New Perspectives,” Mathematics, vol. 11, no. 8, p. 1777, 2023.

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

DOI: http://dx.doi.org/10.24014/ijaidm.v7i1.28803


  • There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Click Here for Information

Journal Indexing:

Google Scholar | ROAD | PKP Index | BASE | ESJI | General Impact Factor | Garuda | Moraref | One Search | Cite Factor | Crossref | WorldCat | Neliti  | SINTA | Dimensions | ICI Index Copernicus