Machine learning-based state of charge estimation: A comparison between CatBoost model and C-BLSTM-AE model
Bibliographic record
Abstract
The State of Charge (SOC) is a key metric within a Lithium-ion battery management system (BMS). Accurate SOC estimation is essential for enhancing battery longevity and ensuring user safety, making it a critical component of an effective BMS. Although SOC estimation has become an active research area for the machine learning (ML) community, only a handful of works have considered its estimation at negative temperatures. This paper proposes the application of two machine learning-based approaches for SOC estimation that perform well at wide range of temperatures (positive and negative) and varying dynamic loads. The first one is a hybrid deep learning approach based on the Convolutional BLSTM Auto-Encoder (C-BLSTM-AE) model that relies on extracting abstract features from input data. The second one is a CatBoost model that leverages the gradient boosting technique to enhance the prediction made by its constituent trees. The performance of the models is evaluated by comparing their regression accuracy and computational resource utilization. The C-BLSTM-AE model achieves a low Mean Absolute Error (MAE) of 0.52% under fixed ambient temperature conditions and maintains a MAE of 1.03% for variable ambient temperatures. The CatBoost model achieves a MAE of 0.69% with fixed temperature settings and a MAE of 1.09% under variable temperature conditions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".