On-Road CO<sub>2</sub> and NO<sub>x</sub> Emissions for a Heavy-Duty Truck with Hydrogen-Diesel Co-Combustion
Bibliographic record
Abstract
Heavy-duty diesel trucking is responsible for 25%-30% of the road transportation CO2 emissions in North America. Retrofitting class-8 trucks with a complementary hydrogen fuelling system makes it possible to co-combust hydrogen and diesel in the existing internal combustion engine (ICE), thus minimizing the costs associated with switching to non-ICE platforms and reducing the barrier for the implementation of low-carbon gaseous fuels such as hydrogen. This retrofitting approach is evaluated based on the exhaust emissions of a converted truck with several thousand kilometres of road data. The heavy-duty truck used here was retrofitted with an air-intake hydrogen injection system, onboard hydrogen storage tanks, and a proprietary hydrogen controller enabling it to operate in hydrogen-diesel co-combustion (HDC) mode. The hydrogen controller operates on the J1939 network, similar to the OEM Controller Area Network (CAN) and determines the hydrogen injection rate from hydrogen energy share ratio (RH2) tables based on engine-related parameters. The cycle-total RH2 for the considered in-use operation ranged from 15% to 28%, with a maximum instantaneous value of close to 40%. This range of RH2 has been explored in engine-dynamometer studies in the literature showing promising results without negative combustion anomalies. Here, the real-drive exhaust CO2 and NOx emissions during the HDC operation were compared to those for the neat diesel operation. The OEM sensors were used for on-road exhaust NOx measurement, and their accuracy and cross-sensitivity to interfering gaseous species were examined in controlled laboratory experiments. The road data shows that the exhaust NOx emissions during the HDC operation are reduced compared to the neat diesel baseline, and the tailpipe CO2 reductions are directly correlated to the hydrogen substitution rates.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.054 | 0.008 |
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".