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Sequential DoE Framework for Steady State Model Based Calibration
Abstract:
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.
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