Li-ion Battery State of Charge Estimation Using Long Short-Term Memory Recurrent Neural Network with Transfer Learning
Why this work is in the frame
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Bibliographic record
Abstract
To develop more efficient, reliable and affordable electrified vehicles, it is very desirable to improve the accuracy of the battery state of charge (SOC) estimation. However, due to the nonlinear, temperature and state of charge dependent behaviour of Li-ion batteries, SOC estimation is still a significant engineering challenge. Traditional methods such as the Kalman filter require significant characterization testing, model development, and filter design and tuning efforts which must be tailored to each battery type. To help solve this problem, this work proposes a novel method to address SOC estimation using a deep neural network (DNN) with Transfer Learning (TL). Transfer learning is a method that uses the learnable parameters from a trained DNN to help train another DNN. Transfer learning has the potential to improve SOC estimation as well as reduce DNN training time and data required. Results show up to 64% better accuracy and similar or better accuracy with a reduced amount of training data.
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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 | 0.000 |
| 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 it