Dynamic modeling of torque & emissions: Digital twin to simulate combustion process and exhaust emissions for diagnostics & prognostics applications

A large fraction of today’s energy consumption is attributable to the transportation sector. Therefore, further development of internal combustion engines has a huge potential to reduce environmental impact through modelling complicated, fast and unsteady phenomena including the changes of emission gases concentration and output torque observed during diesel emission and combustion process. This paper presents research on the emission and combustion characteristics of a heavy vehicle diesel engine, elaborating an engineered framework for prognostics/diagnostics, state monitoring, and performance trending of heavy-duty vehicle engine and after treatment system (ATS). The proposed framework leverages advanced simulation and modeling methodologies to ensure precise predictions and diagnostics. By integrating physics-based models with data-driven techniques, the framework accurately models engine and exhaust system behaviors under various operating conditions. For exhaust system, framework demonstrates encouraging predictive performance in estimating engine/tailpipe NOx-emissions. This development meets stringent emissions regulations and standards by introducing novel method for calculating health scores, particularly for Selective Catalytic Reduction (SCR) systems which enhances diagnostic capabilities, enabling early detection of issues such as reduced conversion efficiency. By accurately predicting emissions and identifying potential problems early, the framework helps ensure compliance with regulatory requirements. Additionally, the framework considers vehicle dynamics, especially in the context of drivetrain health. The Nonlinear Autoregressive with Exogenous Inputs (NARX) model for torque estimation is crucial for understanding the dynamic behavior of the engine and its impact on overall vehicle performance. By monitoring and analyzing deviations in predicted torque, the framework provides insights into the health and performance of the drivetrain, facilitating timely interventions and maintenance actions to ensure optimal vehicle dynamics and reliability. This study presents a framework that integrates emission environment and sustainability, simulation, regulatory compliance, and vehicle dynamics, offering a holistic approach to predictive maintenance for heavy-duty vehicle engines and after-treatment systems.