Task · Powertrain
Emission optimisation
Passing the cycle is a constraint. Choosing where to sit inside it — against fuel, torque and component protection — is a different problem, and it is the one that takes the dyno hours.
In short
Emission optimisation is the work of choosing a calibration that balances competing objectives rather than simply satisfying a limit. Lowering NOx usually costs fuel; protecting a component usually costs torque; improving one cycle point often worsens another. Because the objectives conflict, there is no single best calibration — there is a Pareto front of non-dominated options, and the engineering decision is which point on that front to take. The practical method is to model the responses from a designed experiment rather than measure every combination: a space-filling DoE covers the operating envelope in fewer points than a full factorial grid because it does not re-measure regions the model already predicts well, the responses are fitted as surfaces, and the optimiser then searches the model under explicit constraints. How much dyno time that saves depends on the number of factors, the smoothness of the response and the accuracy required. Emission optimisation is distinct from emission calibration, which is concerned with meeting the certification limit over the cycle.
01
Why there is no single best calibration
The objectives genuinely conflict. Retarding injection timing to cut NOx costs fuel. Enriching to protect a turbine inlet costs both fuel and emissions. Raising EGR to lower NOx raises soot, which loads the filter, which eventually costs fuel again through regeneration. None of these trades is avoidable by being cleverer about one map in isolation.
What that means formally is that the solution is not a point but a set: the calibrations for which no objective can be improved without making another worse. That set is the Pareto front. An optimiser can find it; it cannot tell you which point on it your programme should take, because that is a judgement about what the vehicle is for and what the market requires.
- Conflicting objectivesNOx, soot, fuel consumption, torque, component protection, noise.
- Pareto frontThe set of calibrations where nothing improves without something else degrading.
- ConstraintsLimits that must hold regardless — temperatures, pressures, emission caps.
- The decisionWhich point on the front to take is an engineering choice, not an output.
02
Design of experiments, and what it actually buys
Measuring every combination of factors is not feasible once there are more than a few. A designed experiment places test points to maximise the information gained about the response rather than to tile the space evenly. A space-filling design — Latin hypercube, Sobol sequence, D-optimal — covers the envelope with far fewer points than a full factorial grid, because it does not spend measurements in regions the emerging model already predicts well.
The honest claim is mechanistic rather than numeric. The saving is real but it is a function of how many factors you have, how smooth the responses are, and how much accuracy the decision needs. A programme with two well-behaved factors saves little. One with eight factors and a strongly curved response saves a great deal. Anyone quoting a single percentage across all programmes is quoting a number they cannot defend.
- Static DoESpace-filling plans for steady-state mapping of the envelope.
- Dynamic DoETime-based sequences that capture transient behaviour the steady map misses.
- Safety envelopeOperating limits and rate limits enforced during the test, not after it.
- Model, then searchThe optimiser searches the fitted surfaces, not the engine.
03
Validating the optimum on the real engine
A model-chosen optimum is a hypothesis. It has to be measured at the chosen point and compared against what the surface predicted, because a response surface is only trustworthy inside the region the experiment covered and only as far as its fit allows.
The useful comparison is point by point on one time base: the predicted response, the measured response, and the operating conditions during the run. Where they diverge, the question is usually whether the optimum sits near the edge of the designed region, or whether a factor that was held constant during the experiment was not constant in the vehicle.
04
Where this sits against emission calibration
Emission calibration asks whether the vehicle meets its limits over the certification cycle, and what margin it holds against run-to-run scatter. Emission optimisation asks what else the calibration could be buying while it does so. The first is a compliance question with a pass or fail; the second is a trade-off question with a family of answers.
In a programme they interleave. The optimiser proposes a point, the cycle measurement checks it against the limit with margin, and the result feeds the next iteration. CIE Pro builds the models and searches them; MDX reads the cycle measurement that validates the result; HELIX holds the dataset and the evidence through release.
The instruments
What does what.
- DIGITAL TWIN CALIBRATIONCIE ProDesign the experiment, fit the responses, search the Pareto front under constraints.→
- OSCILLOSCOPE LAB & MCPMDXRead the validation measurement against what the model predicted.→
- RELEASE GOVERNANCE GATEWAYHELIXKeep the chosen dataset and its justification together through release.→
Standards & references
Check the source.
These primary sources define the formats and regulatory context discussed above. They do not certify or endorse VehicleLab products.
Questions
Frequently asked.
What is emission optimisation?
Choosing a calibration that balances conflicting objectives — emissions, fuel consumption, torque and component protection — rather than only satisfying a limit. Because the objectives conflict there is no single best answer, only a Pareto front of options and a decision about which point to take.
How is emission optimisation different from emission calibration?
Emission calibration is about meeting the certification limit over the cycle with enough margin to survive run-to-run scatter. Emission optimisation is about what else the calibration buys while doing so. One has a pass or fail; the other has a family of acceptable answers.
What is a Pareto front in engine calibration?
The set of calibrations for which no objective can be improved without degrading another. An optimiser can compute it from fitted response models; which point on it a programme should take remains an engineering judgement.
Does model-based calibration reduce dyno time?
It reduces the number of measured points needed to characterise a response, because a space-filling design does not re-measure regions the model already predicts well. How much time that saves depends on the number of factors, the smoothness of the response and the accuracy required, so a single percentage quoted across all programmes is not meaningful.
Do you still need to measure the optimum?
Yes. A model-chosen optimum is a hypothesis until it is measured at that point and compared with what the surface predicted. A response surface is only trustworthy inside the region the experiment covered.
Next step
Open one of your own files with us.
Send a measurement, an A2L or an EHANDBOOK container. We will open it in the instruments on a call, with your engineers in the room.