Integrated Electrical and Thermal Model of Li-ion Batteries for Accurate SOC, Core Temperature, and Internal Resistance Estimation
Bibliographic record
Abstract
State of charge (SOC) estimation and thermal management are the key components of electric vehicle (EV) management systems (BMSs). However, accurate estimation is challenging, and battery temperature has a great impact on SOC estimation and battery thermal management. To address these challenges, the proposed model integrates the equivalent circuit model (ECM) and thermal dynamic equations. This model can accurately estimate SOC, terminal voltage, surface and core temperatures, and internal resistance of Li-ion batteries. Experiments are conducted using a battery cell under the Hybrid Pulse Power Characterization (HPPC) test method and a Fiat 500e 2020 electric vehicle battery pack under the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) signals. The experimental results demonstrate that the proposed model achieves an SOC estimation error of less than 1%. Additionally, the model can provide an estimate of internal resistance and core temperature, which can be utilized in state of health (SOH) estimation and thermal management. The application of the proposed model can significantly enhance battery safety, efficiency, and extend the life of EV batteries.
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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) | 0.001 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".