An Unsupervised Domain Adaptation Framework for Cross-Conditions State of Charge Estimation of Lithium-Ion Batteries
Bibliographic record
Abstract
With the rapid development of deep learning (DL), battery state of charge (SOC) estimation has made major strides. However, the batteries’ inconsistency and changing working conditions lead to the distribution discrepancy across domains, which further affects the prediction accuracy of the pre-trained model. Moreover, collecting sufficient and labeled data is labor-intensive to gain a well-performed SOC estimator. To overcome these drawbacks, this article proposes a novel SOC estimation framework based on adversarial domain adaptation. Firstly, a distinctive SOC estimator is constructed and trained to capture the mapping relationship between the original input and the battery SOC based on the offline source dataset with a specific working condition. Then, an adversarial network with a reconstruction module and maximum mean discrepancy (MMD) constraint is designed to extract the domain-invariant features and decrease distribution discrepancy across domains. Thus, the pre-trained model could be transferred to the different working conditions using only the limited and unlabeled target data. Experimental results demonstrate that the best cross-domain root mean square error (RMSE) of the proposed transfer framework is 1.33%, 2.57%, and 1.45% for fixed ambient temperatures, changing ambient temperatures, and changing battery type, respectively, indicating that this framework emerges as a promising solution for the precise battery SOC cross-domain estimation.
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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 | 0.001 |
| 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".