Thermal energy storage for increasing heating performance and efficiency in electric vehicles
Bibliographic record
Abstract
Battery electric vehicles suffer from significant range reduction in extreme cold weather conditions, largely due to the requirement of cabin heating and reduced battery performance. Since heating can require as much energy as the powertrain itself, improving the vehicle thermal energy management can have a substantial impact on range in cold driving conditions. In this paper, sensible and latent thermal energy storage (TES) methods are analyzed in order to improve heating performance and vehicle range in mild to cold weather conditions. To investigate the benefits of TES in electric vehicles, a model was developed in AMESim to simulate cabin heating and its transient interaction with the vehicle's energy systems during a given drive cycle. A thermal energy storage system was developed in the powertrain coolant loop which was integrated with an electric heater and a heat exchanger used for cabin ventilation. In addition to sensible storage, latent thermal storage was also investigated due to its ability to store energy at near constant temperatures. According to the simulation results, a thermal storage can increase the range close to 25%. This benefit is heavily dependent on the storage volume, storage initial temperature, and ambient temperature.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".