Catalyzed particulate filters (CPFs) are used in heavy-duty diesel exhaust systems to reduce the amount of EPA-regulated particulate matter (PM) exiting the tailpipe. During transient engine operation, there is a continual change of PM residing in the filter due to changing rates of PM accumulation and oxidation. Knowing this quantity is important not only for active regeneration control system implementation, but also for quantifying the health of the device. DPF models, required for PM state estimation, exist in the literature but are often not suitable for real-time state estimation due to their computational requirements.

This presentation describes a simple model and state estimation developed using Simulink. The model has been calibrated to experimental data using the Global Optimization Toolbox. An extended Kalman filter is used for state estimation and its performance compared to engine test cell experiments of PM. The filter, also developed using Simulink, could be used for fuel-optimal control strategies as well as on-board diagnostics.