Comparing long short-term memory and gated recurrent unit machine learning techniques for accurate estimation of electric vehicle battery characteristics
Bibliographic record
Abstract
The battery is a crucial factor influencing an electric vehicle's driving range.Low temperatures can significantly reduce the performance of batteries, which can cause irreversible damage, including reduced battery capacity and cycle life.Therefore, maintaining the battery package's temperature and state of charge in low temperatures is critical for ensuring the safety and extending the lifespan of electric vehicle batteries.This paper compares different machine learning techniques, including Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), for estimating battery temperature and state of charge using the BMW i3 winter trips dataset.The effectiveness of these techniques was demonstrated by successfully forecasting battery temperature and state of charge.The accuracy of LSTM and GRU models was evaluated for estimating battery temperature and SOC.LSTM achieved an RMSE of 0.29 °C for battery temperature estimation and 2.23% for SOC estimation.On the other hand, GRU obtained an RMSE of 0.70 °C for battery temperature estimation and 2.52% for SOC estimation.Additionally, the most highly correlated features with battery temperature and state of charge were identified, which can help improve the accuracy of battery estimation models.The analysis highlights the importance of accurate estimation of battery temperature and state of charge for electric vehicle battery management.These machine learning methods could be valuable tools for optimizing battery performance and extending battery lifespan in electric vehicles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| 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".