Creating a Virtual Test Bed Using a Dynamic Engine Model with Integrated Controls to Support in-the-Loop Hardware and Software Optimization and Calibration

Abstract

In the current study, a 0D/1D engine model built in the commercial code GT-Suite was coupled with the Electronic Control Unit (ECU) model created in the Simulink environment, aiming to more accurately predict the interaction of the engine and aftertreatment system (ATS) operating parameters, both during steady-state and transient maneuvers. After a detailed validation based on extensive experimental data from a heavy-duty commercial diesel Internal Combustion Engine (ICE), the engine model was fine-tuned and the 0D predictive diesel combustion model, DIPulse, was calibrated to best predict the combustion process, including engine-out NOx emissions. For correct prediction of the engine’s behavior in transient operations, the complete control strategy of the air path, including boost, exhaust gas recirculation (EGR), main and pilot Start of Injection (SOI), injection pressure, and exhaust flap, was implemented in the Simulink environment. To demonstrate the predictive capability of the model, a hot World Harmonized Transient Cycle (WHTC) was simulated, obtaining good agreement with the experimental data both in terms of emissions and performance parameters, confirming the reliability of the proposed approach. Finally, a case study on possible fuel consumption improvement through thermal insulation of the exhaust manifold, exhaust ports, and turbocharger was carried out.

Real Driving Emissions—Conception of a Data-Driven Calibration Methodology for Hybrid Powertrains Combining Statistical Analysis and Virtual Calibration Platforms

“…The early performance of dynamic tests in virtual test environments enable the prevalidation of components and functions with regard to the subsequent real operating conditions [63]. Targeted modeling of engine and system behavior on MiL and HiL setups allows conclusions to be drawn about emissions, drivability and fuel and energy consumption behavior [71]. This offers frontloading of the emissions calibration and adjusting the operating strategy for hybrid propulsion systems.…”

Real-Time Emission Prediction with Detailed Chemistry under Transient Conditions for Hardware-in-the-Loop Simulations

“…Physics-based mean value approaches have been used to predict the engine behavior throughout the operational driving cycle while enhancing its adaptability through the use of multi-scale or mixed modeling accuracy [21,22]. The implementation of computationally inexpensive algorithms, and the search for lean approaches to optimize the model calibration effort, emissions prediction, and processes, are actual topics of research [23][24][25][26][27][28][29]. On the other hand, the integration of chemistry-based simulation processes to improve accuracy and to enhance the extrapolation capability in extreme ambient conditions has made its way also in both research and industrial applications [30][31][32][33].…”