Cycle-by-Cycle Combustion Optimisation: Calibration of Data-based Models and Improvements of Computational Efficiency. Issue 1 (31st December 2022)

• Record Type: Journal Article
• Authors: Makowicki, Thomas; Bitzer, Matthias; Graichen, Knut
• Is Part Of: Mathematical and computer modelling of dynamical systems. Volume 28:Issue 1(2022)
• Page Start: 110
• Page End: 141
• Publication Date: 2022-12-31
• Subjects: Hybrid model -- model calibration -- combustion optimisation; Engineering -- Mathematical models -- Periodicals; Computer simulation -- Periodicals; 515.39
• DOI: 10.1080/13873954.2022.2052111
• Languages: English
• ISSNs: 1387-3954

Abstract

ABSTRACT: Modern combustion engines require an efficient cycle-by-cycle fuel injection control scheme to optimise the single combustion events during transient operation. The online optimisation of the respective control inputs typically needs accurate while sufficiently simple models of the combustion quantities. Based on a recently presented cycle-by-cycle optimisation scheme with a hybrid model, this paper focuses on two aspects to enhance the accuracy as well as computational efficiency for an online computation. Firstly, the proper calibration of Gaussian processes nested in a combined physics-/data-based model structure is addressed. Respective test bench measurements and a tailored two-step training procedure are presented. Secondly, the computational efficiency of the online cycle-by-cycle optimisation is increased by mapping computationally intensive calculations into the data-based models through offline preprocessing. In addition, a data-driven approximation of the complete optimisation scheme is proposed to further minimise the computational demand. Simulation studies are used to evaluate the performance of these approaches.