### Abstract

Original language | English |
---|---|

Pages (from-to) | 366-394 |

Journal | Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering |

Volume | 225 |

Issue number | D3 |

DOIs | |

Publication status | Published - Feb 2011 |

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### Keywords

- model-based engine calibration
- engine modelling
- Bayesian statistics

### Cite this

*Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering*,

*225*(D3), 366-394. https://doi.org/10.1177/09544070JAUTO1562

**The use of a Bayesian weighted least-squares approach to accelerate empirical engine model generation.** / Brace, C. J.; Akehurst, S.; Ward, M C.

Research output: Contribution to journal › Article

*Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering*, vol. 225, no. D3, pp. 366-394. https://doi.org/10.1177/09544070JAUTO1562

}

TY - JOUR

T1 - The use of a Bayesian weighted least-squares approach to accelerate empirical engine model generation

AU - Brace, C. J.

AU - Akehurst, S.

AU - Ward, M C

PY - 2011/2

Y1 - 2011/2

N2 - An alternative to the commonly used design-of-experiments (DoE) technique based on the use of prior knowledge to speed up the model generation phase of the test programme is presented. Data are gathered in areas where the prior knowledge is least accurate and is blended into an overall empirical model using a Bayesian weighted least-squares (BWLS) approach. The work demonstrates the potential of the BWLS mechanism to reduce the test effort by the incorporation of prior knowledge from neighbouring operating points. Where the prior knowledge is a good representation of the current operating point the number of test points required to allow model convergence may be half that required by a DoE approach. Conversely, the Bayesian technique with inaccurate prior knowledge can still generate an accurate model at the expense of an increased number of test points over a standard experimental design, because the prior will incorrectly influence the model. The algorithm was tested with and without an outlier detection mechanism. This mechanism proved effective in detecting and rejecting simulated outliers and retesting the spurious data point.

AB - An alternative to the commonly used design-of-experiments (DoE) technique based on the use of prior knowledge to speed up the model generation phase of the test programme is presented. Data are gathered in areas where the prior knowledge is least accurate and is blended into an overall empirical model using a Bayesian weighted least-squares (BWLS) approach. The work demonstrates the potential of the BWLS mechanism to reduce the test effort by the incorporation of prior knowledge from neighbouring operating points. Where the prior knowledge is a good representation of the current operating point the number of test points required to allow model convergence may be half that required by a DoE approach. Conversely, the Bayesian technique with inaccurate prior knowledge can still generate an accurate model at the expense of an increased number of test points over a standard experimental design, because the prior will incorrectly influence the model. The algorithm was tested with and without an outlier detection mechanism. This mechanism proved effective in detecting and rejecting simulated outliers and retesting the spurious data point.

KW - model-based engine calibration

KW - engine modelling

KW - Bayesian statistics

UR - http://www.scopus.com/inward/record.url?scp=79951666260&partnerID=8YFLogxK

UR - http://dx.doi.org/10.1177/09544070jAUTO1562

U2 - 10.1177/09544070JAUTO1562

DO - 10.1177/09544070JAUTO1562

M3 - Article

VL - 225

SP - 366

EP - 394

JO - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering

JF - Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering

SN - 0954-4070

IS - D3

ER -