State of Charge Estimation of Lithium-Ion Batteries Using Hybrid Machine Learning Technique
Bibliographic record
Abstract
The pivotal features of low self-discharge, high energy density and long calendar life lead the Lithium-ion (Li-ion) batteries as being a mainstream energy storage source in electric vehicles (EVs). A meticulous estimation of the state of charge (SOC) is indispensable for ensuring safe and reliable operations in battery powered EVs. However, SOC estimation of Li-ion battery with high accuracy have become a major challenge in the automotive industry. To fulfill reliable operation in EVs, researchers have proposed numerous SOC estimators through model based or machine learning techniques. This paper presents an improved SOC estimation of Li-ion battery using random forest (RF) regression, which is robust and effective for controlling dynamic systems. To ensure good resilience and accuracy, a Gaussian filter is adopted at the final stage to minimize the variations in the SOC estimation. The proposed SOC estimator is verified on the experimental data of the Li-ion battery under Federal test driving schedules and different operating temperatures. Results show that the proposed SOC estimator displays sufficient accuracy and outperforms the traditional artificial intelligence based approaches.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".