Adaptive Battery State-of-Charge Estimation using Aging-driven Equivalent Circuit Parameterization and Electrochemical Impedance Spectroscopy
Bibliographic record
Abstract
Ensuring the accurate estimation of lithium-ion battery (LIB) state of charge (SOC) over the entire life cycle of the battery is still an open research challenge due to the nonlinear degradation and calendar aging especially for electric vehicle (EV) applications. State-of-the-art SOC estimation techniques such as Coulomb counting and equivalent circuit model (ECM) in fusion with Kalman filtering are ineffective in ensuring accuracy over the entire cycle life of the battery as the model parameters of these do not accommodate the changes of battery characteristics due to aging. This paper proposes an approach for estimating SOC using an adaptive extended Kalman filter (EKF) based on the parameters obtained from electrochemical impedance spectroscopy (EIS). By updating the ECM parameters, the proposed SOC estimation technique can maintain accuracy throughout the battery life, as the ECM parameters are continuously updated based on feedback from the EIS data. The effectiveness of the proposed method is demonstrated through a series of battery cycling (350 cycles) and EIS test data of a Samsung NCA 21700 LIB cell under a controlled laboratory environment.
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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 | 0.000 | 0.000 |
| 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 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".