Electric Vehicle Range Prediction Models: A Systematic Review of Machine Learning, Mathematical, and Simulation Approaches

Source abstract

This review examines 80 research studies on electric vehicle (EV) range prediction published between 2013 and 2024. We categorized all studies into three methodological groups such as machine learning (ML), mathematical modeling (MM), and simulation modeling (SM). The analysis reveals a clear dominance of ML models (48.8% of studies), followed by simulation models (32.5%), mathematical models (12.5%), and hybrid models (6.2%). Among the ML techniques, Neural Networks (25%), Multiple Linear Regression (17.5%), and Decision Trees (16.25%) were the most frequently employed, highlighting the growing emphasis on data-driven and adaptive methods. While simulation techniques are most prevalent within MM studies. Hybrid models, which integrate multiple methods, are gaining popularity for improving prediction accuracy. We also reviewed performance metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) which reflect the diversity of evaluation strategies across the field. We highlight unsolved challenges including robust feature selection, real-time data integration, and battery degradation modeling. Finally, We suggest future research should focus on combining different modeling approaches, using more advanced data-driven methods, and improving reliability through data sharing and collaboration.