Lithium-Ion Battery State of Charge Estimation With Adaptability to Changing Conditions
Bibliographic record
Abstract
Accurate state of charge (SOC) estimation provides critical information to ensure the safe operation of lithium-ion batteries (LIBs). With the evolution of artificial intelligence, the model-free deep learning-based SOC estimation has made extraordinary progress. However, collecting sufficient data to train an accurate SOC estimator of batteries' whole lifecycle is costly, and generalizing the estimator to different operating conditions is challenging. To this end, aiming to enhance the generalization ability of data-driven SOC estimators, this article proposes a novel SOC estimation framework based on adversarial transfer learning (TL), which transfers a well-trained SOC estimator with fewer data demands to fit different conditions. Specifically, a source SOC estimator is trained under a specific condition to learn the basic characteristics of the battery, which is defined as the source domain. Target domains correspond to measured data of the battery under different conditions. An adversarial TL-based training framework is developed to extract the domain invariants of the source and target domains, which is guided by minimizing the distribution discrepancies. The effectiveness of the proposed method is demonstrated through the use of two LIBs datasets. The successful generalization to multiple operating conditions and aging health states demonstrates the estimation accuracy, robustness, and good generalization.
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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 | 0.001 | 0.001 |
| 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".