Estimation of model parameters and state-of-charge for battery management system of Li-ion battery in EVs
Bibliographic record
Abstract
The Battery Management System (BMS) is responsible for accurate monitoring of the status of the battery (State-of-Charge (SOC)) for maintaining optimal battery performance in Battery Electric Vehicles (BEVs). Ambient temperature is a significant factor that influences the accuracy of SOC estimation, hence electrochemical combined model dependents of temperature was utilized for simulating the dynamic behavior of battery in BMS. Unknown parameters of the battery model are identified using the least square algorithm for Dynamic Stress Test (DST), validation of estimation is conducted for Federal Urban Driving Schedule (FUDS) and concluded that the error between predicated terminal voltage form model and voltage from DST profile was less than 0.08V for defined conditions. Then, for SOC estimation, recursive Bayesian estimation method based Extended Kalman Filtering (EKF), and Sigma-Point Kalman Filtering (SPKF) approaches were adopted. To quantify the performance of the estimators, Root Mean Square Error (RMSE) and execution time at different temperature were evaluated. The evaluation results indicate that maximum error in case of EKF is 2.43% whereas for SPKF is 1.2% and maximum execution time taken by EKF is 3.57 sec whereas for SPKF is 4.53 sec. The results reported that SPKF provides accurate and robust SOC estimation in compared EKF and could be efficiently applied in BMS for BEVS.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".