A Model Cluster Adapting to Different Charge Voltage Segments for Battery Capacity Estimation
Bibliographic record
Abstract
Abstract Accurate capacity estimation is vital for the management of lithium-ion batteries in Electric Vehicles (EVs). Data-driven methods using the battery charging process provide new insights for battery capacity estimation. However, extracting features from the complete or specific charge curves is difficult as the battery charging is related to the behavior of the drivers, e.g., the battery start state of charge (SOC) and end SOC are usually random. Therefore, this study proposes a framework using a model cluster for the capacity estimation of lithium-ion batteries, which uses multi-submodels adapting to different lengths of input charge voltage segments, where input features are extracted. Three datasets (NCA, NCM, and Oxford datasets) are employed to establish the model cluster, and three types of input features and four algorithms are compared. The Random Forest (RF) algorithm combined with the time vector (input feature) achieves the best estimation results on the NCA dataset, in which the Root Mean Square Errors (RMSEs) of most submodels are lower than 1%. Thus, submodels with RMSE lower than 1% are retained to form the model cluster. The NCM dataset is used for the model cluster verification, and all RMSEs are below 0.74%. Three probability distributions of the charging process are constructed based on the three datasets to fit the model cluster to the actual EV operation situation, and the maximum RMSE is 0.403%, which provides a new perspective on the battery capacity estimation for EVs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".