Development of a fast-running injector model with artificial neural network (ANN) for the prediction of injection rate with multiple injections RWTH Aachen University

Format:

Book

Conference/Event

Author/Creator:

Golc, Dominik, author.

Contributor:

Beeckmann, Joachim

Esposito, Stefania

Pitsch, Heinz

Conference Name:

15th International Conference on Engines & Vehicles (2021-09-12 : Capri, Italy)

Language:

English

Physical Description:

1 online resource cm

Place of Publication:

Warrendale, PA SAE International 2021

Summary:

The most challenging part of the engine combustion development is the reduction of pollutants (e.g. CO, THC, NOx, soot, et cetera) and CO2 emissions. In order to achieve this goal, new combustion techniques are required, which enable a clean and efficient combustion. For compression ignition engines, combustion rate shaping, which manipulates the injected fuel mass to control the in-cylinder pressure trace and the combustion rate itself, turned out to be a promising opportunity. One opportunity to enable this technology is the usage of specially developed rate shaping injectors, which can control the injection rate continuously. This is not possible with standard series hardware. A feasible solution with series injectors is the usage of multiple injections to control the injection rate and, therefore, the combustion rate. For the control of the combustion profile, a detailed injector model is required for predicting the amount of injected fuel. Simplified 0D models can easily predict single injection rates with low deviation. However, the prediction of injection rates with multiple injections is more challenging, because of the impact of past injections on future ones. In this work, an advanced 0D injector model is presented, which takes into account the effect of injection history. In order to develop and calibrate the model, an injection rate testbench has been used to generate an extensive and suitable database. This database is used to train an artificial neural network (ANN), which is integrated in the model. The developed multi-injection model predicts with high accuracy (R²>0.85) the experimental injection rate up to four injections. Additionally, the model is real-time capable and therefore usable for controller application

Notes:

Vendor supplied data

Publisher Number:

2021-24-0027

Access Restriction:

Restricted for use by site license