Hybrid State of Charge Estimation Approach for Lithium-ion Batteries using k-Nearest Neighbour and Gaussian Filter-based Error Cancellation
Bibliographic record
Abstract
Lithium-ion batteries have emerged as a mainstream source to store energy in electrified vehicles due to pivotal features of high energy density, long cycle life and low self-discharge. The continuous monitoring of state of charge (SOC) of a lithium-ion battery is essential to avoid over-charging or over-discharging in order to ensure safe operation as well as to reduce its average life cycle cost. However, an accurate SOC estimation of lithiumion battery has become a major challenge in the automotive industry. In this paper, k-nearest neighbours (kNN) concept have been employed to estimate the SOC, based on the measured voltage, current and previous SOC. A Gaussian filter is employed to minimize the errors in the kNN-based SOC estimation. The effectiveness of the proposed hybrid method is verified on the experimental data of the lithium-ion battery under different standard driving schedules and temperatures. Results show that the proposed hybrid approach outperforms other conventional SOC approaches with better accuracy under federal urban and highway driving schedules.
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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".