Temperature Dependent State of Charge Estimation of Lithium-ion Batteries Using Long Short-Term Memory Network and Kalman Filter
Bibliographic record
Abstract
In this paper, a new state of charge (SOC) estimation method is proposed combining Long Short-Term Memory Network (LSTMN) battery model and Kalman filter (KF) considering temperature dependency. The technique has been compared to the equivalent circuit model (ECM) and the combined model (CM) using dynamic stress test data. A KF is applied to each model to realize the dynamic estimation of battery states. Based on the collected data from the federal urban driving schedule, terminal voltage approximation and SOC estimation are carried out, and the results are compared among the models. This paper includes the following contributions: (1). A LSTMN battery model that shows stronger robustness against temperature is implemented. (2). A LSTMN-KF method is proposed for SOC estimation at different temperatures and is compared with ECM-KF method and CM-KF method. (3). The proposed method eliminates the need for SOC-OCV lookup table and does not rely on the chemical characteristics of batteries.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".