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Electric Vehicle Range Prediction

Mannava Divya Teja, Nuthalapati Ganesh, Mutyala Bhuvan Saieesh


The introduction of new energy vehicles has emerged as a new trend in the automotive industry in
response to growing energy and environmental issues. The electric vehicle (EV) is the driving force behind new
energy vehicles. The one major problem electric vehicles have always been the distance(range) of the travel and map
to the nearby charging stations. For range prediction in the present study, four machine learning
algorithms—multiple linear regression, random forest regression, polynomial regression, and support vector
regression—are employed and contrasted with the most recent research. Python implementation is used for a
simulation model that takes the coordinates of the starting point, endpoint, Map of the surrounding location land, and
the locations of charging stations to predict an optimal path for the vehicle to travel. We use an A* path-finding
algorithm to traverse the black-and-white map and find the optimal path from source to destination. This work
proposes a novel Machine learning (ML) based predictive strategy to estimate the driving range of electric vehicles
and route them to the nearby charging stations. The range predictor considers the specific vehicle parameters over a
distributed network of charging stations. The charging stations are modeled as entities with many charging points
and, in each stage of the prediction the status of availability of charging points is monitored and updated to a cloud
database. A better overview of the driving range and vehicle’s energy consumption may help reduce the overall range
anxiety of many EV drivers.

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Sun, S., Zhang, J., Bi, J., & Wang, Y. (2019). A Machine Learning Method for Predicting Driving Range of Battery Electric Vehicles. Journal of Advanced Transportation, 2019, 1– 14. doi:10.1155/2019/4109148.

Vidal, C., Malysz, P., Kollmeyer, P., & Emadi, A. (2020). Machine Learning Applied to Electrified Vehicle Battery State of Charge and State of Health Estimation: State-of-the-Art. IEEE Access, 8, 52796–52814. doi:10.1109/access.2020.2980961.

Ferreira, J. C., Monteiro, V., & Afonso, J. L. (2013). Dynamic range prediction for an electric vehicle. 2013 World Electric Vehicle Symposium and Exhibition (EVS27). doi:10.1109/evs.2013.6914832.

P. Ondrús̆ ka and I. Posner, & Probabilistic attainability maps: Efficiently predicting driver-specific electric vehicle range,& 2014 IEEE Intelligent Vehicles Symposium Proceedings, 2014, pp. 1169-1174, doi:


Bi, Jun & Wang, Yongxing, Shao, Sai Cheng, Yang. (2018). Residual Range Estimation for Battery Electric Vehicle Based on Radial Basis Function Neural Network. Measurement. 128. 197- 203.


J. A. Oliva, C. Weihrauch, and T. Bertram, Model-based remaining driving range prediction in electric vehicles

by using particle filtering and Markov chains, 2013 World Electric Vehicle Symposium and Exhibition (EVS27),

, pp. 1-10, doi: 10.1109/EVS.2013.6914989.

Kruppok, Kurt & Walter, Tobias & Kriesten, Reiner & Sax, Eric. (2018). Improving Range Prediction of Battery Electric Vehicles by Periodical Calculation of Driver Parameters Based on Real Driving Data.


Z. Wang, X. H. Wang, L. Z. Wang, X. F. Hu and W. H. Fan; Research on electric vehicle (EV) driving range prediction method based on PSO- LSSVM, 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), 2017, pp. 260-265, doi:10.1109/ICPHM.2017.7998338.

McKinnon, A.: ‘Green logistics: improving the environmental sustainability of logistics’, Transp. Rev. A Transnat L. Transdisciplinary J., 2001, 31, (4), pp. 547–548

Kleindorfer, P.R., Singhal, K., Wassenhove, L.N.V.: ‘Sustainable operations management’, Prod. Oper. Manage., 2005, 14, (4), pp. 482–492.



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