Numerical assessment of thermal management on the capacity fade of lithium-ion batteries in electric vehicles
Bibliographic record
Abstract
Electric vehicles, as a major strategy for climate change mitigation, uses lithium-ion batteries extensively as the power source. However, the operation, performance and lifetime of lithium-ion batteries depend on the battery temperature, which can have a wide range due to heat generation within the battery and significant variations in the ambient conditions due to changes in seasons and geographical locations where electric vehicles are operated. In the present study, thermal management methods/strategies on the capacity fade of lithium-ion batteries are assessed through a validated capacity fade model for lithium-ion batteries along with a thermal model for the heat generation in the battery and dissipation over battery surface, represented by various thermal management methods. The driving conditions are simulated through a constant and various standard drive cycles. It is shown that battery temperature has the predominant impact on the capacity fade, and it can be controlled through effective thermal management. A much more significant spread in battery capacity fade occurs with various thermal management methods for a lower initial battery temperature (20°C) compared to the higher temperatures (35°C and 50°C), hence, thermal management is much more effective in reducing capacity fade at battery temperatures close to 20°C, which is considered the optimum operating temperature for lithium-ion batteries. Further, the results indicate that using a lower charge voltage can result in slightly less capacity fade over cycling. Regenerative braking makes it more realistic to use lower charge voltages, since the battery can be recharged during operation, thereby increasing driving range, while preventing increased capacity fade. Effective thermal management is more imperative for realistic intense and aggressive driving behaviors.
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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 | 0.000 | 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".