Xlj Xander Seykens

Cylinder pressure-based control in heavy-duty EGR diesel engines using a virtual heat release and emission sensor

This paper presents a cylinder pressure-based control (CPBC) system for conventional diesel combustion with high EGR levels. Besides the commonly applied heat release estimation, the CPBC system is extended with a new virtual NO x and PM sensor. Using available cylinder pressure information, these emissions are estimated using a physically-based combustion model. This opens the route to advanced On-Board Diagnostics and to optimized fuel consumption and emissions during all operating conditions. The potential of closed-loop CA50 and IMEP control is demonstrated on a multi-cylinder heavy-duty EGR engine. For uncalibrated injectors and fuel variations, the combustion control system makes the engine performance robust for the applied variations and reduces the need for a time consuming calibration process. Cylinder balancing is shown to enable auto-calibration of fuel injectors and to enhance fuel flexibility. For both Biodiesel and US diesel, the effects on NO x and PM emissions are partly compensated for by combined CA50 and IMEP control. This can be further improved by application of (virtual) emission sensors. Furthermore, it is shown that this combustion controller shows good transient performance during load changes. The virtual emission sensor is successfully implemented for real-time control. For operating conditions with high EGR rates and varying injection timing, the predictions of the virtual NO x and PM sensor are compared with measurements. NO x emission prediction inaccuracy is typically on the order of 12%, which is comparable to commercially available sensors. The predicted PM emissions show good qualitative agreement, but need further improvement for application in DPF regeneration and PM emission control strategies. Robust emission control is essential to meet future requirements for On-Board Diagnostics and In-Use Compliance.

Experimental validation of extended NO and Soot model for advanced HD Diesel Engine Combustion

A computationally efficient engine model is developed based on an extended NO emission model and state-of-the-art soot model. The model predicts exhaust NO and soot emission for both conventional and advanced, high-EGR (up to 50%), heavy-duty DI diesel combustion. Modeling activities have aimed at limiting the computational effort while maintaining a sound physical/chemical basis. The main inputs to the model are the fuel injection rate profile, in-cylinder pressure data and trapped in-cylinder conditions together with basic fuel spray information. Obtaining accurate values for these inputs is part of the model validation process which is thoroughly described. Modeling results are compared with single-cylinder as well as multi-cylinder heavy-duty diesel engine data. NO and soot level predictions show good agreement with measurement data for conventional and high-EGR combustion with conventional timing.

Experimental validation of a dynamic waste heat recovery system model for control purposes

This paper presents the identification and validation of a dynamic Waste Heat Recovery (WHR) system model. Driven by upcoming CO2 emission targets and increasing fuel costs, engine exhaust gas heat utilization has recently attracted much attention to improve fuel efficiency, especially for heavy-duty automotive applications. In this study, we focus on a Euro-VI heavy-duty diesel engine, which is equipped with a Waste Heat Recovery system based on an Organic Rankine Cycle. The applied model, which combines first principle modelling with stationary component models, covers the two-phase flow behavior and the effect of control inputs. Furthermore, it describes the interaction with the engine on both gas and drivetrain side. Using engine dynamometer measurements, an optimal fit of unknown model parameters is determined for stationary operating points. From model validation, it is concluded that the identified model shows good accuracy in steady-state and can reasonably capture the most important dynamics over a wide range of operating conditions. The resulting real-time model is suitable for model-based control. Copyright

Automated model fit method for diesel engine control development

This paper presents an automated fit for a control-oriented physics-based diesel engine combustion model. This method is based on the combination of a dedicated measurement procedure and structured approach to fit the required combustion model parameters. Only a data set is required that is considered to be standard for engine testing. The potential of the automated fit tool is demonstrated for two different heavy-duty diesel engines. This demonstrates that the combustion model and model fit methodology is not engine specific. Comparison of model and experimental results shows accurate prediction of in-cylinder peak pressure, IMEP, CA10, and CA50 over a wide operating range. This makes the model suitable for closed-loop combustion control development. However, NO emission prediction has to be improved.