Model-Guided Data-Driven Optimization for Automotive Compression Ignition Engine Systems

Abstract

Essentially, the performance improvement of automotive systems is a multi-objective optimization problem [1–4] due to the challenges in both operation management and control. The interconnected dynamics inside the automotive system normally requires precise tuning and coordination of accessible system inputs. In the past, such optimization problems have been approximately solved through expensive calibration procedures or an off-line local model-based approaches where either a regressive model or a first-principle model is used. The model-based optimization provides the advantage of finding the optimal model parameters to allow the model to be used to predict the real system behavior reasonably [5]. However, other than the model complexities, there are practically two issues facing the integrity of these models: modeling uncertainty due to inaccurate parameter values and/or unmodeled dynamics, and locally effective range around operating points. As a result, the optimum solutions extracted from the model-based approach could be subject to failure of expected performance [6].

Model guided extremum seeking control of electromagnetic micromirrors

“…Recently, Refs. 11 , 12 proposed the concept of offline model-guided extremum seeking optimization for the optimal calibration of engine compression ignition and fuel injection optimization, which has the capability to accelerate convergence with the support of known models.…”