Exploratory data analysis for electric vehicle driving range prediction: insights and evaluation

Debani Prasad Mishra, Prince Kumar, Priyanka Rai, Ayush Kumar, Surender Reddy Salkuti


One of the biggest challenges of electric vehicle (EV) users has been predicting the amount of driving time their vehicles will have on one battery charge. Planning a trip and reducing range anxiety depends on an accurate range estimate. This study aims to anticipate the EV driving range using machine learning methods. In this research, several regression models for predicting EV driving range will be developed and compared. A real-world dataset comprising various factors affecting EV range, such as power, trip distance, energy consumption, driving style, and environmental factors, is used for analysis. The dataset is preprocessed using exploratory data analysis methods to manage missing values, outliers, and categorical variables. The findings of this study contribute to the expanding area of EV range prediction and provide EV buyers, producers, and regulators with insightful information. The user experience can be improved, EV adoption can be boosted, and effective charging infrastructure design is made possible with accurate range prediction. The study also highlights the importance of model selection and data pretreatment in making accurate predictions.


deep multilayer perceptron; electric vehicle; exploratory data analysis; machine learning; range prediction

Full Text:


DOI: http://doi.org/10.11591/ijape.v13.i2.pp474-482


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Applied Power Engineering (IJAPE)
p-ISSN 2252-8792, e-ISSN 2722-2624

Web Analytics Made Easy - StatCounter IJAPE Visitors