Exergy Analysis of an On-Vehicle Floating Piston Hydrogen Compression System for Direct-Injection Engines
Bibliographic record
Abstract
Direct injection of hydrogen at high pressures into an otherwise unmodified heavy-duty diesel engine offers a near-term pathway to near-zero greenhouse gas emissions for commercial vehicles. Hydrogen direct-injection engines maintain diesel-like performance with equal or better thermal efficiency. Supplying the hydrogen for injection pressures of ~30 MPa requires a high-pressure supply. Onboard hydrogen compression enables more complete utilization of the stored compressed hydrogen; however, it introduces a significant parasitic load on the engine. The magnitude of this load depends on factors such as the compressor’s configuration, capacity, pressure ratio, efficiency, and the engine’s operating conditions. This paper presents an exergy analysis of an onboard hydrogen compression system that uses hydraulically driven free-floating pistons, sized for heavy-duty commercial vehicles. Minimizing the parasitic loads from the compressor is essential to retain vehicle performance and maximize system-wide efficiency. The exergy analysis approach provides a comprehensive understanding of the whole compression system by comparably quantifying the losses across all components. A one-dimensional model of the compression system, developed in GT-SUITETM and validated with experimental data, is used to quantify the main exergy loss components. Exergy efficiency ranges from 12% to 45% under varying pressure ratios and cycle frequencies, with a pronounced increase in efficiency observed at higher cycle frequencies. Major exergy losses occur in the hydraulic driving system up to 79%, especially during retracting and idle phases for lower pressure ratios and cycle frequencies. Within the compression cylinder, exergy destructions account for less than 10% of the total work input, wherein heat transfer and piston friction are identified as the dominant contributors to exergy destruction, with their effects intensifying at higher pressure ratios. This work highlights the challenges of onboard gas compression and develops a systematic framework that can compare compressor design alternatives for different driving cycles.
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.
How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".