Comparing Traditional and Machine Learning Models for Battery SOC Calculation
Bibliographic record
Abstract
The automotive industry is currently investing heavily to pivot from conventional internal combustion engines to electric powertrains. The battery pack still makes up a large portion of each electric vehicle’s cost, making management and control of the battery crucial for this transition. With improved state of charge estimation an increased amount of energy can safely be extracted from each battery pack, therefore increasing the range, or allowing smaller packs to be utilized. For this study, traditional equivalent circuit models and machine learning methods are compared for battery state of charge estimation across a temperature range of -10°C to 25°C. The machine learning models explored include support vector regression, feedforward neural networks and recurrent neural networks. The dataset used was derived from various tests and drive cycles performed on a Panasonic 18650PF NMC Cell in a temperature chamber. Based upon the results, it is evident that equivalent circuit models perform very well at higher temperatures, but struggle to capture the highly nonlinear characteristics of batteries at lower temperatures. On the other hand, when properly trained and parameterized, machine learning models can be much more effective at capturing the battery’s characteristics at low temperatures while maintaining their computational requirements low.
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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".