US 10,788,396 B2 · Using an artificial neural network for combustion phasing control in a spark ignited internal combustion engine

Abstract

A calibration system and method for a spark ignition engine of a vehicle involve artificially weighting engine dynamometer data in high engine load regions and using it to generate training data for an artificial neutral network (ANN). A plurality of ANNs are trained using the training data and the plurality of ANNs are then filtered based on their maximum error to obtain a filtered set of trained ANNs. A statistical analysis is performed on each of the filtered set of trained ANNs including determining a set of statistical metrics for each of the filtered set of trained ANNs and then one of the filtered set of trained ANNs having a best combination of error at high engine loads and the set of statistical error metrics is then selected. Finally, an ANN calibration is generated using the selected one of the filtered set of trained ANNs.

Claims16

Dependent claims

A calibration system for a spark ignition (SI) engine of a vehicle, the calibration system comprising:

dynamometer sensors configured to measure dynamometer data for the SI engine;
and
a computing system configured to:
receive the dynamometer data for the SI engine,
artificially weight the dynamometer data in high engine load regions to obtain weighted engine dynamometer data,
generate training data for an artificial neural network (ANN) using the weighted engine dynamometer data,
train a plurality of ANNs using the generated training data,
filter the plurality of trained ANNs based on their maximum error to obtain a filtered set of trained ANNs,
perform a statistical analysis on each of the filtered set of trained ANNs including determining a set of statistical metrics for each of the filtered set of trained ANNs,
select one of the filtered set of trained ANNs having a best combination of error at high engine loads and the set of statistical metrics, and
generate an ANN calibration using the selected one of the filtered set of trained ANNs.

8 more elements

A calibration method for a spark ignition (SI) engine of a vehicle, the method comprising:

receiving, by a calibration system, dynamometer data for the SI engine;
artificially weighting by the calibration system, the dynamometer data in high engine load regions to obtain weighted engine dynamometer data;
generating, by the calibration system, training data for an artificial neural network (ANN) using the weighted engine dynamometer data;
training, by the calibration system, a plurality of ANNs using the generated training data;
filtering, by the calibration system, the plurality of trained ANNs based on their maximum error to obtain a filtered set of trained ANNs;
performing, by the calibration system, a statistical analysis on each of the filtered set of trained ANNs including determining a set of statistical metrics for each of the filtered set of trained ANNs;
selecting, by the calibration system, one of the filtered set of trained ANNs having a best combination of error at high engine loads and the set of statistical metrics;
and
generating, by the calibration system, an ANN calibration using the selected one of the filtered set of trained ANNs.

6 more elements

Dependencies are inferred from the claim text.

Using an artificial neural network for combustion phasing control in a spark ignited internal combustion engine

Life of this patent

Using an artificial neural network for combustion phasing control in a spark ignited internal combustion engine

Life of this patent

Abstract

Claims16