Akademik Veri Yönetim Sistemi
IET Intelligent Transport Systems, cilt.19, sa.1, 2025 (SCI-Expanded)
Electric vehicles (EVs) provide significant advantages for sustainable transportation, such as reduced energy consumption, the ability to integrate with renewable energy sources, and emission reductions. Nevertheless, range anxiety, high battery costs, and long charging times limit the adoption of EVs. Accurately estimating driving range is one of the solutions to overcome these limitations. This study proposes a method that combines an extra tree regressor (ETR) model and an artificial rabbit optimization (ARO) algorithm to predict the driving distance using a comprehensive dataset for EVs. In our experiments, we compared ARO with well-known hyperparameter optimization methods such as grid search (GS) and random search (RS), and tested the models across multiple train and test splits. Besides using the complete feature set, we applied recursive feature elimination (RFE) to select an informative subset and re-evaluated all methods. With all features, the best configuration of the proposed algorithm achieved an R-squared (R2) of 0.84, a root mean square error (RMSE) of 14.38, a mean absolute error (MAE) of 7.70, and a mean squared error (MSE) of 220.12. Using the selected subset of seven features, the proposed model reached an R2 of 0.84, with an RMSE of 14.88, an MAE of 6.75, and an MSE of 221.53. Finally, the contribution of each feature's to the predicted driving range was analysed using shapely additive explanations (SHAP). The findings of the study emphasize the value of integrating machine learning (ML) models and hyperparameter search methods into electric vehicle range-estimation systems to improve driver confidence and support sustainable transportation.This study advances the current understanding of range prediction and contributes to reducing range anxiety, thereby supporting extensive adoption of EVs. The findings of the study indicate that the integration of ML approaches in the range estimation of EVs can play a critical role in increasing driver confidence and supporting sustainable transportation. This study contributes to the existing knowledge in the field of range estimation and is an important step toward the broader adoption of EVs.