SCR on Filter (SCRoF) is an efficient and compact NOX and PM reduction technology already used in series production for light-duty applications. The technology is now finding its way into the medium duty and heavy duty market. One of the key challenges for successful application is the robustness to real world variations. The solution to this challenge can be found by using model-based control algorithms, utilizing state estimation by physics-based catalyst models.
This paper focuses on the development, validation and real time implementation of a physics-based control oriented SCRoF model. An overview of the developed model will be presented, together with a brief description of the model parameter identification and validation process using engine test bench measurement data. The model parameters are identified following a streamlined approach, focusing on decoupling the effects of deNOx and soot phenomena. Model validation results will be shown demonstrating good model performance for both steady state and transient conditions.
Following the validation of the off-line SCRoF model, the model was implemented on a dSPACE platform to demonstrate its real-time capability. The implementation on the dSPACE system is verified by comparing the online model signal outputs to the offline model. Further on, the model is compiled into object code and will be integrated into the ECM. The embedded model is characterized by a low memory footprint and a fast turnaround time.