In recent years, the automotive industry has shifted from purely combustion engine-driven vehicles towards hybridization due to the introduction of CO2 emission legislation. Hybrid powertrains also represent an important pillar and starting point in the journey towards zero-emission and full electrification. Fulfilling the most recent emission standards requires efficient control strategies for the engine, capable of real-time operation. Model accuracy is one of the main parameters which directly influence the performance of such control strategies. Specific methodologies developed in the past, such as physically- or phenomenologically-based approaches, have already facilitated the modeling of the combustion engine. Even though these models can accurately predict emissions in steady state conditions, their performance during transient engine operation is time-consuming and still not sufficiently reliable. The major contribution of the current work is to clarify and apply the recent advancements in data-driven modeling techniques, especially in time series forecasting with feedforward neural networks (FFNNs) and long short-term memory networks (LSTMs), to address the limitations mentioned above and to compare the different approaches.
The quantity and quality of data are significant challenges for data-driven modeling. This paper studies the modeling of gasoline engine emissions using FFNNs and LSTMs. The data quantity and quality requirements are studied based on a portable emission measurement system (PEMS), measuring at 1 Hz, and additional analyses on an engine test bench with a HiL setup, providing the possibility of increasing the measurement frequency with more sophisticated devices by a factor of five. Subsequently, the training and validation of the FFNNs and LSTMs are outlined, and finally, the model accuracy is discussed.