Plug-In Hybrid Vehicle Thermal Management and System Operation in Real-World Conditions
Bibliographic record
Abstract
Plug-in hybrid electric vehicles (PHEVs) use stored electrical energy from electrical grids as well as chemical energy from fuel as energy sources for propulsion and various auxiliary loads. Using their electrified powertrains, PHEVs are designed to achieve improved overall efficiency and lower emissions compared to conventional internal combustion engine vehicles (ICEVs). Real-world conditions may, however, require significant thermal management energy, lowering PHEV efficiency and increasing their tailpipe emissions. A series of on-road tests were completed in Canadian summer and winter conditions to characterize the operation of four 2018 model-year PHEVs: a minivan, an SUV, and two hatchbacks. Their respective thermal management equipment and associated control strategies are discussed in this paper. Vehicle equipment included electric air-conditioning systems, electric coolant heaters, advanced heat pump systems, and conventional heater core-based systems. The results are analyzed in terms of operation strategy, thermal management loads, tailpipe CO2 emissions, and various electric range metrics. In summertime, all vehicles were able to run all-electrically for significant distances, thanks to their electric air-conditioning systems. In wintertime, the two vehicles with electric coolant heaters immediately started their engines to boost heat production, but also used stored electrical energy to provide heat and/or propulsion. One vehicle had an advanced heat pump system, which allowed it to run all-electrically for significant portions of winter testing (albeit for shorter distances than in summer testing). The remaining test vehicle had no electrical heating capability, and thus required engine operation whenever cabin heat was needed, but blended-in electrical energy for propulsion when possible.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".