Battery State of Charge Estimation by Machine Learning
Bibliographic record
Abstract
Accurate estimation of State of Charge (SOC) is essential for managing lithium-ion batteries used in electric vehicles and energy storage systems. This paper presents a comparative study of four different machine learning models: Multivariate Linear Regression (MLR), Random Forest (RF), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNN) for the prediction of SOC using a data set based on LG 18650HG2 cells. The prepared data set includes voltage, current, temperature and historical moving averages, sampled at 1 Hz and evaluated in a temperature range of -10°C to 25°C. SOC values were derived through normalized amp-hour integration accounting for polarity and nominal capacity. Each model was evaluated under both full (5-variable) and reduced (3-variable) input configurations (excluding average current and voltage). The results show that Random Forest achieved the highest accuracy in all cases, with R2values exceeding 0.9997 and Mean Absolute Percentage Error (MAPE) below 0.40%. CNN models also performed well, particularly in capturing sequential patterns, while MLP offered a balanced performance between CNN and MLR in most scenarios, with a MAPE of 9.43%. MLR demonstrated the lowest robustness under thermal variation. The findings in this paper highlight that ensemble-based decision tree models may outperform both neural network models and traditional regression techniques for real-time SOC estimation in practical battery management systems, especially on smaller battery datasets. These findings offer practical insights for implementing efficient and scalable SOC estimation models in real time BMS.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".