Gaussian mixture regression and local linear network model for data-driven estimation of air mass

The conventional approaches for modelling of charge cycle in combustion engines are based on first principles. In this approaches, it is necessary to estimate different parameters in engine, which is very difficult to implement in real-time. In addition, it requires a high parameterisation effort because of a variety of engine characteristics. The model is further used in engine control unit to improve the efficiency of the combustion. Moreover, it can be used for estimation of some physical variables in engine to increase the performance of the controller. It also avoids the installation of extra sensors and reduces the costs of production. In this study, two different data-driven modelling methods for estimation of air mass are investigated. They also offer a flexible modelling approach considering various options for equipment as well as sensors and actuators in the engines. Both data-driven methods, the Gaussian mixture regression (GMR) and the local linear model tree (LOLIMOT) algorithm, allow a flexible modelling with a high input space dimensionality, with sparsity of data, a good interpretability and also offer possibilities of adaptation and local optimisation. While the GMR uses a statistical approaches to train the model, in the LOLIMOT an incremental, heuristic approach is used. Both methods are applied to the data obtained from a spark-ignition engine and the results are compared and discussed.