Development of a Fully Physical Vehicle Model for Off-Line Powertrain Optimization: A Virtual Approach to Engine Calibration
Bibliographic record
Abstract
Nowadays control system development in the automotive industry is evolving rapidly due to several factors. On the one hand legislation tightening is asking for simultaneous emission reduction and efficiency increase, on the other hand the complexity of the powertrain is increasing due to the spreading of electrification. Those factors are pushing for strong design parallelization and frontloading, thus requiring engine calibration to be moved much earlier in the V-Cycle. In this context, this paper shows how, coupling well known physical 1D engine models featuring predictive combustion and emission models with a fully physical aftertreatment system model and longitudinal vehicle model, a powerful virtual test rig can be built. This virtual test rig can be used for powertrain virtual calibration activities with reduced requirement in terms of experimental data. This work moved from an already developed and validated powertrain and vehicle model featuring a 1.6-liter diesel engine Fast Running Model (FRM) with DIPulse predictive combustion and emissions model. On one hand, the engine model was calibrated on 29 steady state operating points and validated on a full engine map, showing a maximum error below 5% on Brake Specific Fuel Consumption (BSFC) and an average error around 20% for NOx emissions. On the other hand, the vehicle and aftertreatment model, composed by Diesel Oxidation Catalyst (DOC) and Selective Catalyst Reduction on Filter (SCRoF), was validated on the Worldwide Harmonized Light Duty Vehicles Test Cycle (WLTC) in terms of fuel consumption, engine-out and tailpipe NOx emissions. This virtual test rig was then used to optimize the engine calibration in a fully automated way, exploiting NSGA-III (Non dominated Sorting Genetic Algorithm) and strong parallelization capabilities. The optimization considered different design constraints, including also the combustion noise and was performed over a set of Key Points (KPs) representative of the engine operating conditions along WLTC, RTS95, US06 and FTP75. 10 independent variables were considered including both fuel injection and air management control variables. Output of the optimization was the Pareto front BSFC-Noise-NOx per each operating point. This intermediate result could directly be used by calibration engineers to select the most appropriate calibration set. Moreover, the Pareto fronts were used in an additional optimization loop to develop various calibration sets, each of which with a different weight for NOx emissions and engine fuel consumption. Finally, the optimized engine calibrations were assessed over the WLTC. The fully virtual approach was so demonstrated to be capable to achieve comparable results with respect to traditional experimental engine calibration methods at a fraction of time and cost and before any vehicle experimental activity.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".