Thermal Management of a Hybrid Electric Vehicle in Cold Weather
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Cold weather is an important matter for electric and hybrid-electric vehicles (EV/HEVs), because the electro-chemical process is slowed at low temperatures, hence inducing a loss of power and energy. To prevent it from happening, battery packs are heated via a battery thermal management system (BTMS). However, in some cases, HEVs/EVs are not connected to the grid, so the BTMS uses energy from the battery pack, thus impacting total autonomy. Strategies exist, but must be optimized. Therefore, this paper proposes the design of a HEV strategy that is parked outside and unplugged from the electrical grid. Its objective is to find a compromise between the cost in energy contained in the battery and the aging of the cells. The optimization is based on dynamic programming and utilizes an electro-thermal battery model including aging. Simulation results are then compared with other strategies, underlining that parking outside at work or overnight without plugging in are issues with the actual strategies of common EVs/HEVs, mainly because aging as a result of cold temperatures is not considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it