An electrochemical model-based particle filter approach for Lithium-ion battery estimation
Bibliographic record
Abstract
Lithium-ion batteries are currently amongst the leading technologies for electrical energy storage. In automotive industry they are recognized as the most promising alternative to gasoline powered engines. State estimation of the state of the battery can provide useful information regarding the state of charge (SOC) and state of health (SOH) of the battery which play a crucial role in optimal and safe utilization of the battery. Although the electrochemical dynamics of the battery are described by nonlinear system of PDAEs, most works in the area of condition monitoring of the battery resort to empirical or equivalent electrical circuit models. These models don't provide any physical insight into the battery and lack insight into physical limitations of the battery. This work presents a particle filter algorithm for state estimation and condition monitoring of the Li-ion battery. This filter can effectively deal with the nonlinear and complex nature of the PDAEs describing the dynamics of the battery. It provides accurate estimation of the average as well as spatial distribution of concentration in the battery. The simulation results demonstrate the effectiveness of the proposed estimation algorithm.
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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".