Experimental Measurement of On-Road CO2 Emission and Fuel Consumption Functions
Bibliographic record
Abstract
Motorized transport has become an essential part of our world economic system with an ever-increasing number of vehicles on the road. However, considering the depletion of energy resources and the aggravation of greenhouse gas issues, it is critical to improve vehicle fuel consumption. These demands are moving us toward advanced engine and powertrain technologies. However, understanding our progress also requires improvements in the way we measure and certify vehicle emissions and fuel economy performance. This paper describes the use of an on-board fuel consumption and emissions measurement system to develop on-road fuel consumption functions that can be used to quantify the fuel economy impact of vehicle, road and traffic control changes. The system uses an ECM OBD-II scanner, a Mass Air Flow meter and an emissions analyzer to monitor fuel consumption and exhaust CO2 emission rates (in g/s) as well as vehicle speed and other parameters. All measurements are coordinated and recorded using a laptop computer. Vehicle tractive power is calculated from speed measurements using vehicle dynamic models, allowing calculation of actual fuel efficiency. In the results, the measured CO2 emission values correlate well with those predicted by a carbon balance from measured fuel consumption, confirming the validity of a range of measurements. This paper reports on fuel consumption behaviors for five typical vehicles over seventy repeated tests in urban, highway and aggressive driving situations. Although it is well known that vehicle energy demand goes up with increasing steady speed, the results show the strong importance of fuel efficiency, vehicle accelerations and idle periods for actual on-road fuel consumption. Fuel efficiency is essentially zero at idle but rises to a high level for vehicle tractive power over 30% of the rated power. This trend indicates the potential for reduced fuel consumption through engine down-sizing and powertrain controls. For vehicles running in normal traffic situations, the fuel consumption tends to be best in the 60 km/h to 100 km/h average vehicle speed range due to the reduced severity of accelerations and lack of idling. Those results emphasize the potential for fuel savings through improvements of road structure and traffic control to reduce congestion. The test results are used to generate a fuel consumption model based on a vehicle dynamic model and speed trace. This model can be used to quantify the fuel consumption and greenhouse gas CO2 emission effect for changes in vehicle characteristics and on-road operating conditions.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".