Electric Vehicle Analytics & Performance Prediction

Abstract

Electric Vehicles (EVs) are central to the decarbonization of transportation, yet their real-world performance depends on many factors including battery characteristics, vehicle parameters, driving patterns, environmental conditions, and charging behavior. This paper presents an end-to-end analytical framework—combining data preprocessing, exploratory data analysis, and machine learning—to predict EV performance metrics (notably driving range and energy consumption). We evaluate multiple models, including Linear Regression, Random Forest, and Gradient Boosting, and use cross-validation and error metrics (MAE, RMSE, R²) to compare their predictive power. The proposed framework also identifies the most influential features affecting EV performance and demonstrates how data-driven insights can inform vehicle design and user practices.

Introduction

Electric vehicle (EV) performance depends on factors like battery capacity, charging time, energy consumption, vehicle specifications, driving patterns, and environmental conditions. Accurate prediction of these parameters aids manufacturers in optimizing design, battery usage, and overall efficiency. Traditional evaluation methods are costly and time-consuming, motivating the use of data analytics and machine learning.

This project proposes EV-Analytics, a data-driven framework for analyzing and predicting EV performance using real-world datasets. The system performs data preprocessing, exploratory data analysis (EDA), feature extraction, and machine learning modeling to identify key performance factors and generate predictive models. Algorithms such as Linear Regression, Random Forest, and Gradient Boosting are applied, with models evaluated using MAE, RMSE, and R² metrics.

The framework allows accurate prediction of driving range, battery efficiency, and energy consumption, enabling improved EV performance assessment. By integrating machine learning with EV data, the system supports battery management, vehicle optimization, and sustainable transportation planning, offering a systematic approach for analyzing and enhancing electric vehicle efficiency.

Conclusion

This research presents an Electric Vehicle Analytics & Performance Prediction system that utilizes data analytics and machine learning techniques to analyze electric vehicle datasets and predict key performance indicators such as driving range and energy efficiency. The proposed framework includes data preprocessing, exploratory data analysis, feature engineering, machine learning model training, and performance evaluation to effectively analyze EV performance patterns. The experimental results demonstrate that machine learning models such as Linear Regression, Random Forest, and Gradient Boosting can successfully learn the relationships between EV parameters such as battery capacity, charging time, energy consumption, and vehicle specifications. Among these models, ensemble-based algorithms showed improved prediction accuracy due to their ability to capture complex patterns in the dataset. The use of visualization techniques and evaluation metrics such as MAE, RMSE, R² score, ROC analysis, and confusion matrix further helped validate the performance and reliability of the proposed system. These analyses highlight the importance of battery capacity and energy efficiency as key factors influencing electric vehicle performance. Overall, the proposed EV analytics framework demonstrates the potential of machine learning in improving electric vehicle performance analysis and supporting the development of more efficient and sustainable transportation systems. Future improvements can include the integration of real-time EV sensor data and advanced deep learning techniques to further enhance prediction accuracy and system scalability.

References

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Copyright

Copyright © 2026 A. Bharath Kumar, A. Sandeep Kumar Reddy, C. Prudhvi Reddy, D. Dhanush Reddy, D. Umaheswar Reddy, T. PRINCESS RAICHEL. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.