Estimation of the remaining charge retention time of an electric vehicle battery
Bibliographic record
Abstract
Accurately estimating the remaining driving time (RDT) of an electric vehicle (EV) battery is essential for optimizing energy management and enhancing user experience. However, traditional estimation methods do not adequately account for the influence of temperature, driving characteristics and vehicle driving time, leading to less accurate predictions and suboptimal range management. To address these limitations, this study presents a method for estimating the remaining charge retention time by integrating temperature and driving characteristics, which refines predictions and improves model reliability. Furthermore, data from the National Big Data Alliance for New Energy Vehicles (NDANEV) were employed to develop a predictive model based on machine learning (ML) models. The different ML models compared in this study are Linear Regression, LSTM, RF, Prophet, LightGBM, and XGBoost. The model performance was evaluated using mean absolute error (MAE), root mean square error (RMSE), the coefficient of determination ( R 2 ) and the prediction runtime to assess the prediction accuracy. The results show that the R 2 values for Prophet, Random Forest, LSTM, XGBoost, and LightGBM are 0.91, 0.94, 0.95, 0.94, and 0.94 respectively. This suggests that XGBoost outperforms the other models, providing the most accurate estimate of the remaining driving time. In addition, the result confirms that considering driving characteristics and ambient temperature improves the reliability and robustness of estimations. These advancements contribute to more efficient energy management and optimized charging strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".