Sequential DoE Framework for Steady State Model Based Calibration

Event
SAE 2013 World Congress & Exhibition
Authors Abstract
Content
The complexity of powertrain calibration has increased significantly with the development and introduction of new technologies to improve fuel economy and performance while meeting increasingly stringent emissions legislation with given time and cost constraints. This paper presents research to improve the model-based engine calibration optimization using an integrated sequential Design of Experiments (DoE) strategy for engine mapping experiments. This DoE strategy is based on a coherent framework for a model building - model validation sequence underpinned by Optimal Latin Hypercube (OLH) space filling DoEs. The paper describes the algorithm development and implementation for generating the OLH space filling DoEs based on a Permutation Genetic Algorithm (PermGA), subsequently modified to support optimal infill strategies for the model building - model validation sequence and to deal with constrained non-orthogonal variables space.
The development, implementation and validation of the proposed strategy is discussed in conjunction with a case study of a GDI engine steady state mapping, focused on the development of an optimal calibration for CO₂ and particulate number (Pn) emissions. The proposed DoE framework applied to the GDI engine mapping task combines a screening space filling DoE with a flexible sequence of model building - model validation mapping DoEs, all based on optimal DoE test plan augmentation using space filling criteria. The case study results show that the sequential DoE strategy offers a flexible way of carrying out the engine mapping experiments, maximizing the information gained and ensuring that a satisfactory quality model is achieved.
Meta TagsDetails
DOI
https://doi.org/10.4271/2013-01-0972
Pages
13
Citation
Kianifar, M., Campean, L., and Richardson, D., "Sequential DoE Framework for Steady State Model Based Calibration," Engines 6(2):843-855, 2013, https://doi.org/10.4271/2013-01-0972.
Additional Details
Publisher
Published
Apr 8, 2013
Product Code
2013-01-0972
Content Type
Journal Article
Language
English