As a journey to green initiatives, one of the focus areas for automotive industry is reducing environmental impact especially in case of internal combustion engines. Latest digital twin technology enable 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 architecture for prognostics/diagnostics, state monitoring, and performance trending of heavy-duty vehicle engine (HDVE) and after treatment system (ATS).
The proposed architecture leverages advanced modeling methodologies to ensure precise predictions and diagnostics, using data-driven techniques, the architecture accurately model’s engine and exhaust system behaviors under various operating conditions. For exhaust system, architecture demonstrates encouraging predictive performance in estimating engine/tailpipe NOx-emissions. This development introduces 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 architecture helps ensure compliance with regulatory requirements. Additionally, the architecture 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 architecture 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 an architecture that integrates emission and vehicle dynamics models with prognostics health asset framework offering a holistic approach to predictive maintenance for HDVE and ATS