Ensemble-Based Robust Model for Accurate Driving Range Estimation of EVs Leveraging Big Data
Bibliographic record
Abstract
In the emergence of greener transportation, electric vehicles (EVs) play an important role, where the accurate pre-diction of the driving range is pivotal for alleviating driver range anxiety, serving as a foundation for spatial planning, operational strategies, and efficient charging infrastructure management. This study addresses the challenge of limited driving range in EVs and introduces an ensemble-based machine learning (ML) model for predicting the vehicle's driving range. By employing big data collected from the real world, this work investigates the appropriateness of various ML models, including extreme learning model (ELM), extreme gradient boosting (XGBoost), multiple linear regression (MLR), multilayer perceptron (MLP), deep MLP, random forests (RF), AdaBoost, and support vector regression (SVR) for the problem stated above. Extensive experimental analysis proves that the ensemble framework that exploits MLR and XGBoost predictors surmounts the existing solutions. It achieves an R2 score greater than 0.9 on both training and testing data subsets, exhibiting no overfitting issues, and boasting acceptable inference time. The findings offer a compelling solution for enhancing the estimation of EV driving range for practical applications.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".