A Quantitative Approach for Mapping and Assessing Diesel Particulate Filter Regeneration Events in Diesel Engines

Regeneration of diesel particulate filters (DPFs) is crucial for maintaining the performance of diesel engines and minimizing harmful particulate matter (PM) emissions from exhaust. However, conventional regeneration strategies often suffer from incomplete soot removal and inefficient monitoring. These issues lead to increased exhaust back pressure, reducing engine efficiency, and potentially damaging the particulate filter. In this paper, an approach is proposed for mapping and quantifying the real-world DPF regeneration process for diesel engines complying with the stringent emission standards. We introduce a novel metric, the differential pressure drop percentage (DPDP), to detect regeneration events and quantify soot burn quality. The proposed method utilizes real-time sensor data obtained through the vehicle’s On-Board Diagnostics (OBD) system. The algorithm processes sensor data and robustly maps the regeneration quality. The performance of regeneration event detection and soot burn quality has been validated based on diagnostic trouble codes (DTCs) raised by the engine control unit (ECU). Our proposed method demonstrates that predictive maintenance can be used to manage strategies for diesel exhaust after-treatment systems, which can effectively reduce increased maintenance costs and operational downtime.