iTransformer Based Voltage Estimation of Lithium-Ion Batteries
Bibliographic record
Abstract
Accurate battery voltage modeling is fundamental to optimizing performance, ensuring safety, and extending battery life in Electric Vehicles (EVs). Precise voltage modeling also enables unswerving estimates of range, reducing range anxiety for drivers. Conventional battery voltage models have been equivalent circuit models or other empirical models. However, these methods frequently fall short in accurately capturing the complex characteristics of batteries. To overcome these limitations, this paper introduces the iTransformer machine learning model as an alternative for voltage prediction in EV batteries. iTransformers enhance time-series predictions by applying attention across variates rather than time steps, improving accuracy for multivariate tasks like battery voltage modeling, while maintaining the parallel processing efficiency of transformers. Its inverted architecture enables it to better capture cross-feature relationships and temporal dependencies, as seen in battery data, while maintaining computational efficiency. Our experimental results demonstrate the model's effectiveness, achieving an RMSE of 30 mV and an MAE of 21 mV on the test dataset from an LG 18650HG2 Li-ion cell, which are within the range required in most practical applications, indicating high accuracy for battery voltage modeling.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".