The significant range degradation of new energy vehicles(NEVs)in low-temperature environments necessitates accurate remaining mileage prediction to alleviate range anxiety,optimize energy management strategies,and enh...The significant range degradation of new energy vehicles(NEVs)in low-temperature environments necessitates accurate remaining mileage prediction to alleviate range anxiety,optimize energy management strategies,and enhance battery performance.This research analyzes real-world driving data from 30 NEVs collected between January and March in 2023 and 2024,implementing systematic data cleaning,feature engineering,and model development.The data processing phase addressed temporal inconsistencies,missing values,and outliers through time-series reorganization,interpolation techniques,and segment filtering.A state-ofcharge(SOC)threshold-based segmentation strategy substantially increased the effective sample size.In feature extraction,22 characteristics were derived from driving behavior patterns,battery conditions,and static vehicle parameters,with 14 core features selected through correlation analysis.The incorporation of nominal battery energy and driving range enhanced model representation capabilities.The model development utilized a multi-vehicle data fusion approach based on the Boosting Tree algorithm as the foundation.A hierarchical modeling strategy improved predictions for short-range vehicles,while incremental learning enabled dynamic model updates to accommodate time-varying factors such as battery degradation.Experimental validation demonstrated root mean square errors(RMSE)of 24.483 km for long-range vehicles and 6.425 km for short-range vehicles,markedly superior to conventional single-vehicle models.This methodology not only enhances prediction accuracy under low-temperature conditions but also presents novel technical approaches for NEV battery management and energy optimization.展开更多
文摘The significant range degradation of new energy vehicles(NEVs)in low-temperature environments necessitates accurate remaining mileage prediction to alleviate range anxiety,optimize energy management strategies,and enhance battery performance.This research analyzes real-world driving data from 30 NEVs collected between January and March in 2023 and 2024,implementing systematic data cleaning,feature engineering,and model development.The data processing phase addressed temporal inconsistencies,missing values,and outliers through time-series reorganization,interpolation techniques,and segment filtering.A state-ofcharge(SOC)threshold-based segmentation strategy substantially increased the effective sample size.In feature extraction,22 characteristics were derived from driving behavior patterns,battery conditions,and static vehicle parameters,with 14 core features selected through correlation analysis.The incorporation of nominal battery energy and driving range enhanced model representation capabilities.The model development utilized a multi-vehicle data fusion approach based on the Boosting Tree algorithm as the foundation.A hierarchical modeling strategy improved predictions for short-range vehicles,while incremental learning enabled dynamic model updates to accommodate time-varying factors such as battery degradation.Experimental validation demonstrated root mean square errors(RMSE)of 24.483 km for long-range vehicles and 6.425 km for short-range vehicles,markedly superior to conventional single-vehicle models.This methodology not only enhances prediction accuracy under low-temperature conditions but also presents novel technical approaches for NEV battery management and energy optimization.
