Prediction of Battery Electric Vehicle Energy Consumption via Pre-Trained Model Under Inconsistent Feature Spaces

Accurately predicting the trip-level energy consumption of battery electric vehicles (BEVs) can alleviate range anxiety of drivers and improve intelligent route planning. However, although data-driven methods excel in predicting with multi-feature inputs, each vehicle often requires a dedicated model due to potential inconsistencies in feature spaces of collected data. Consequently, the necessity of sufficient trip data challenges newly registered vehicles. To address the challenges, this study proposed a transformer-based pre-trained model for BEV energy consumption prediction adapting to inconsistent feature spaces, referred to as IFS-Former. By innovatively introducing trainable missing-feature embeddings and placeholder masks, the IFS-Former can tolerate new or missing features of downstream tasks after pre-training. The IFS-Former was pre-trained on a dataset comprising 837 vehicles from 8 different cities, containing 492 thousand trips, and validated on 13 vehicles with inconsistent feature spaces. After applying transfer learning to the 13 vehicles, the pre-trained IFS-Former attains high prediction accuracy (R2 = 0.97, mean absolute error (MAE) = 1.19). Even under extremely inconsistent feature spaces, the IFS-Former maintains robust performance (R2 = 0.96, MAE = 1.31) leveraging its pre-trained knowledge. Furthermore, the IFS-Former is well-suited for on-board deployment with a size of only 32 MB. This study facilitates on-board artificial intelligence for accurate and practical energy consumption prediction.