Connections Between Temperature Consistency in Battery Pack and Driving Condition of Electric Vehicles: A Naturalistic Driving Study
Bibliographic record
Abstract
To prevent battery thermal runaway for electric vehicles (EVs), it is necessary to figure out and apply the connections between temperature consistency in battery pack (TCBP) and driving condition to achieve accurate evaluation and diagnosis for temperature inconsistency. This article designed and conducted the naturalistic driving experiments on EVs, and the long-term and high-frequency vehicular running data was used to explore the connection characteristics between TCBP and driving condition for the first time. The microtrip method was adopted to divide EVs’ running segments, and 24 driving condition parameters (DCPs) are extracted for each segment. The principal component analysis (PCA) and k-means algorithm were used to cluster segments into congested, moderate, and smooth driving condition (SDC). For the three driving conditions, the correlation between DCPs and variation coefficient of probe temperature (VCPT) was obtained by calculating their maximum information coefficient (MIC). The importance and influence pattern of DCPs to VCPT was analyzed using random forest (RF) model and ALEs plot, and their quantitative effect on VCPT was calculated by data statistics. Moreover, key DCPs preferably used for TCBP estimation or prediction modeling were identified. The research results provide important insights for the development of adaptive threshold-based evaluation and diagnosis method for temperature inconsistency in EVs’ battery pack.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".