Battery Dual Extended Kalman Filter State of Charge and Health Estimation Strategy for Traction Applications
Bibliographic record
Abstract
The growing market of electrified vehicles requires efforts from car manufacturers to build robust systems to deal with all types of situations their products will face in customers’ hands. A major system of electrified vehicles is the energy storage unit. The complexity of batteries lies in their nonlinear behaviour that is highly dependent on external factors such as temperature and load dynamics. To handle these conditions, the battery management system relies on algorithms that estimate the state of the storage unit. State of charge (SOC) estimation, which is widely studied in industry and academia, is commonly considered one of the most significant functions of a battery management system (BMS). State of health (SOH) estimation is likewise important as it is necessary to support more consistent SOC and state of power (SOP) estimation. In this paper, a dual Extended Kalman Filter (DEKF) model is proposed to estimate the battery state of charge and capacity state of health across the battery lifespan. The DEKF model is demonstrated to accurately estimate SOC as the battery ages, with an average RMS error of 1.0% for SOH varying from 100% to 80%. The model is also shown to be robust against initial SOC and sensor error, demonstrating its applicability to real world conditions.
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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 | 0.000 | 0.000 |
| 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".