Interacting Multiple Model Strategy for Electric Vehicle Batteries State of Charge/Health/ Power Estimation
Bibliographic record
Abstract
States estimation of lithium-ion batteries is an essential element of Battery Management Systems (BMS) to meet the safety and performance requirements of electric and hybrid vehicles. Accurate estimations of the battery's State of Charge (SoC), State of Health (SoH), and State of Power (SoP) are essential for safe and effective operation of the vehicle. They need to remain accurate despite the changing characteristics of the battery as it ages. This paper proposes an online adaptive strategy for high accuracy estimation of SoC, SoH and SoP to be implemented onboard of a BMS. A third-order equivalent circuit model structure is considered with its state vector augmented with two more variables for estimation including the internal resistance and SoC bias. An Interacting Multiple Model (IMM) strategy with a Smooth Variable Structure Filter (SVSF) is then employed to determine the SoC, internal resistance, and SoC bias of a battery. The IMM strategy results in the generation of a mode probability that is related to battery aging. This mode probability is then combined with an estimation of the battery's internal resistance to determine the SoH. The estimated internal resistance and the SoC are then used to determine the battery SoP which provides a complete estimation of the battery states of operation and condition. The efficacy of the proposed condition-monitoring strategy is tested and validated using experimental data obtained from accelerated aging tests conducted on Lithium Polymer automotive battery cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".