Closed-Loop Predictive Control of a Multi-mode Engine Including Homogeneous Charge Compression Ignition, Partially Premixed Charge Compression Ignition, and Reactivity Controlled Compression Ignition Modes
Bibliographic record
Abstract
<div>High thermal efficiency and low engine-out emissions including nitrogen oxides (NOx) and particulate matter (PM) make low-temperature combustion (LTC) favorable for use in engine technologies. Homogeneous charge compression ignition (HCCI), partially premixed charge compression ignition (PPCI), and reactivity controlled compression ignition (RCCI) are among the common LTC modes. These three LTC modes can be achieved on the same dual-fuel engine platform; thus, an engine controller can choose the best LTC mode for each target engine load and speed. To this end, a multi-mode engine controller is needed to adjust the engine control variables for each LTC mode.</div> <div>This article presents a model-based control development of a 2.0-liter multi-mode LTC engine for cycle-to-cycle combustion control. The engine is equipped with port fuel injectors (PFI) and direct injectors (DI). All combustion modes are achieved with dual fuels (iso-octane and <i>n</i>-heptane) under naturally aspirated conditions. Using experimental data, control-oriented models (COMs) are developed for HCCI, PPCI, and RCCI combustion modes on a cycle-to-cycle basis. The COMs for HCCI, PPCI, and RCCI modes can predict the combustion phasing (CA50, the crank angle by which 50% of the fuel mass is burned) with average errors of 1.3 crank angle degrees (CAD), 1.5 CAD, and 1 CAD, respectively. The average errors in predicting the indicated mean effective pressure (IMEP) for HCCI, PPCI, and RCCI modes are 18 kPa, 34 kPa, and 43 kPa, respectively. Multi-input and multi-output (MIMO) adaptive model predictive controllers (MPCs) with linear parameter varying (LPV) models are designed for the LTC modes. CA50 and IMEP are controlled by adjusting the premixed ratio (PR) of the fuels, start of injection (SOI) timing, and fuel quantity (FQ). The results show that the designed MPCs are able to track both CA50 and IMEP in all combustion modes, with average tracking errors of less than 1 CAD and 5.2 kPa, respectively.</div>
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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) | 0.001 | 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 | 0.001 |
| 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".