Development of an Electro-Thermal Model for Electric Vehicles Using a Design of Experiments Approach
Bibliographic record
Abstract
An accurate and computationally efficient lithium-ion battery model is beneficial when developing state-of-charge (SOC) and state-of-health (SOH) algorithms for battery management systems (BMS). These models allow for software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing, where the battery pack is simulated in software. However, development of these battery models can be time-consuming, especially when trying to model the effects of temperature and SOC on the equivalent circuit model (ECM) parameters. Estimation of this relationship is often accomplished by carrying out many experiments, which can be costly and time consuming for BMS manufacturers. To address these issues, this paper makes two contributions to literature. First, a comprehensive battery model is developed, where the ECM parameter surface is generated using a design of experiments (DOE) approach. Second, replication runs are conducted to accurately estimate the measurement noise and determine which model parameters are significant. The technique is then compared with existing approaches from the literature, and it is shown that, by using the proposed method, the same degree of accuracy can be obtained while requiring significantly fewer experimental runs. This can be advantageous for BMS manufacturers that require a high-fidelity model but cannot afford to carry out many experiments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 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) | 0.004 | 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".