On-Road and Dynamometer Evaluation of Vehicle Auxiliary Loads
Bibliographic record
Abstract
Laboratory and on-road vehicle evaluation is conducted on four vehicle models to evaluate and characterize the impacts to fuel economy of real-world auxiliary loads. The four vehicle models in this study include the Volkswagen Jetta TDI, Mazda 3 i-ELOOP, Chevrolet Cruze Diesel, and Honda Civic GX (CNG). Four vehicles of each model are included in this; sixteen vehicles in total. Evaluation was conducted using a chassis dynamometer over standard drive cycles as well as twelve months of on-road driving across a wide range of road and environmental conditions. The information gathered in the study serves as a baseline to quantify future improvements in auxiliary load reduction technology. The results from this study directly support automotive manufacturers in regards to potential “off-cycle” fuel economy credits as part of the Corporate Average Fuel Economy (CAFE) regulations, in which credit is provided for advanced technologies in which reduction of energy consumption from vehicle auxiliary loads can be demonstrated. The observed on-road auxiliary load varied from 135 W to over 1200 W across a wide range of ambient conditions and utilization patterns. The annual average auxiliary load varied across vehicle models from 310 W to 640 W. Ambient temperature was the most predominant factor to impact auxiliary load since air conditioner (A/C) operation is prevalent at high ambient temperature and heating system operation is prevalent at cold ambient temperatures. Additionally the impact of auxiliary load on vehicle fuel economy was determined to be typically between 7.5% and 18% of the fuel consumed during onroad operation of the four vehicle models in this study. During dynamometer testing, auxiliary loads were captured from several key locations along the low-voltage bus, including the alternator output, the low-voltage battery, and select other locations dependent upon the vehicle configuration. Dynamometer testing was then conducted on both certification and custom constant-speed drive cycles at three ambient temperatures (-7 °C, 23 °C, as well as 35 °C with 850 W/m2 of solar emulation). This instrumentation and test methodology provides an accurate understanding of the energy use by the accessory system from these four vehicle technologies. This paper details and discusses the dynamometer and on-road evaluation results of the auxiliary load from the sixteen vehicles over the twelve month period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".