Dedicated hybrid engine technology using auxiliary electronic components has been proven as an energy-saving solution to public concerns about energy consumption and carbon emissions. This paper proposes a generic approach of K-fold filter-collaborated feature selection (KFFC-FS) to robustly identify the key parameters of three engine performance indicators, i.e., volumetric efficiency, thermal efficiency, and fuel consumption. By using this approach, five filters are collaborated to provide a robust rank of feature importance and avoid the feature overestimation caused by the single filter. Meanwhile, the K-fold cross validation method is introduced to avoid random precision issues and overfitting, further enhancing the robustness of key parameter identification for the independent engine performance indicators. In this research, the modelling data is collected from an experimental test bench with a BYD 1.5L gasoline engine. Under the basics of the studied three engine performance indicators by using a multiple-layer perceptron network, the proposed approach further reduces by at least 10.3% root-mean-square error (RMSE) and at least 30% reduction of the model inputs.

The accurate prediction of soft sensors is essential for the development of modern combustion engines to achieve better performance, lower emissions, and reduced fuel consumption. To precisely predict engine performance, i.e., indicated thermal efficiency, volumetric efficiency, and fuel consumption rate of a hybrid engine, in this article, we propose a novel data-driven approach of statistics-guided accelerated swarm feature selection to find the most effective features for engine soft sensors. Differing from the existing filter or wrapper feature selection approaches, this approach uses external measure information to direct velocity updates in the accelerated swarm feature selection. Several filter and wrapper methods are developed and comprehensively compared. The experimental dataset is collected from a BYD 1.5 L gasoline engine. Validated by bench test, the results demonstrate that the proposed approach finds the most effective features and optimal network structure for data-driven performance prediction of the hybrid engine that was studied.