Ultra Low Emission Norms Project Development by Virtualization - An Efficient Combination of Virtual and Conventional Test Benches
Bibliographic record
Abstract
The ever-increasing cost of automotive powertrain development is due to the more complex technologies required to meet the latest emissions legislation and customer expectations. Manufacturers need to conduct extensive development loops of test bench and on-road testing to verify the hardware, emission control system, corresponding ECU software function development. Increased resources are required to build up a comparably large number of prototype vehicles to calibrate all the ECU algorithms and functionalities. Increasing powertrain complexity leads typically to a strong increase of conventional calibration efforts. Therefore, there is a strongly increasing need for an advanced calibration approach based on multi-facial XiL simulation. This holistic simulation approach combines the statistical MiL simulation method used for validation of in-field system performance and a HiL based virtual calibration method that fully virtualizes the vehicle and powertrain in combination with a real hardware ECU. The method provides an adequate interactive process merging various model-based methodologies and allows its professional roll-out to the vehicle calibration process. Practically proven powertrain and vehicle modelling approaches are introduced as the fundamentals of integrated XiL based virtual calibration. It is hereby demonstrated that XiL simulation is already useful to assist in early calibration phases. But it is also highly valuable at later stages for system characterization of IUPV. Major contribution of this virtual test bench approach is for calibration of base engine, EATS and OBD functions. The method reduces 30 - 40% of time / effort and 35+ % prototype vehicles [4]. Several derivatives in different applications, transmissions, power ratings, 2WD and 4WD can be served. In this paper, showcasing how a right sized and early integration of virtualization methods can fulfil various development aspects of upcoming challenges. Advantages regarding quality and cost improvements are explained using selected representative examples, which are mandatory to comply with upcoming ultra-low emission standards.
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 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".