Estimation of the remaining charge retention time of an electric vehicle battery
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it