Browse Topic: Particulate filters
Stricter environmental legislation is driving ever-more-demanding performance targets for gasoline particulate filters (GPFs). This study constructs a multi-scale filtration model based on fractal characteristics, taking into account particle size distribution and particle deposition, to investigate the influence of the microstructure of porous media on GPF performance and analyze the impact of structural parameters on capture efficiency and pressure drop. The results show that: (1) Increasing the wall thickness can improve the capture efficiency and pressure drop, and a thicker wall has a stronger inertial interception capacity for larger particles. (2) A reduction in porosity markedly alters both filtration efficacy and flow pressure drop. For particles in the intermediate size range (0.1-0.5 μm), the capture efficiency of a low-porosity structure is more sensitive to the diffusion deposition of small particles, while the inertial collision efficiency of large particles is higher. (3) Shrinking the pore size markedly enhances capture efficiency while simultaneously increasing pressure drop; the finer pore network markedly improves the retention of sub-micron particles, but the passage restriction of large particles is more obvious.
This paper presents the collaborative efforts of the USCAR GPF OBD Working Group to evaluate and recommend On-Board Diagnostic (OBD) monitoring requirements for Gasoline Particulate Filters (GPFs). The group, comprising representatives from major OEMs, aims to establish a unified understanding of GPF monitoring capabilities and propose regulatory recommendations to CARB. The paper outlines the physics of soot generation and oxidation, regulatory interpretations, and diagnostic strategies, culminating in a proposed framework for GPF OBD compliance. The material in this paper was previously presented at the 2024 SAE OBD Symposium [1].
The legislation of CEV Stage V emission norms has necessitated advanced Diesel Particulate Filter calibration strategies to ensure optimal performance across diverse construction equipment applications in the Indian market. Considering the various duty cycles of cranes, backhoe loaders, forklifts, compactors, graders, and other equipment, different load conditions and operational environments require a comprehensive strategy to enhance DPF efficiency, minimize regeneration frequency, and maintain compliance with emission standards. The DPF, as an after-treatment system in the exhaust layout, is essential for meeting emission standards, as it effectively traps particulate matter. Regeneration occurs periodically to burn the soot particles trapped inside the DPF through ECU management. Therefore, understanding soot loading and in-brick DPF temperature behavior across various applications is key. This paper explores the challenges in DPF calibration for CEV Stage V and provides a comprehensive approach to address these challenges, including optimizing soot loading and thermal management for different duty cycles across various applications within a unified calibration framework. The frugal Off-Highway Vehicle market expects a leaner Exhaust Gas Treatment approach, which increases the challenges of thermal management and soot loading. Additionally, the market is moving towards extracting maximum BMEP from their engines, which impacts passive regeneration and DPF thermal stability, among other parameters.
Komatsu has launched a new excavator, the PC220LCi-12, that features its latest intelligent machine control technology. IMC 3.0 incorporates automation enhancements and a reported “construction-industry first” technology - factory-integrated 3D boundary control - designed to boost operator productivity. The intelligent machine, displayed previously at Bauma 2025 in Munich, Germany, has many of the same features as the new PC220LC-12 excavator, including a cab that is 28% larger, with 30% more legroom and 50% improved visibility compared to the PC210LC-11 model. Other advantages the new machines offer are up to a 20% increase in fuel efficiency thanks to a new electrohydraulic system and 129-kW (173-hp) next-generation engine, and up to a 20% reduction in maintenance costs due to longer replacement intervals for hydraulic oil and oil filters and longer cleaning intervals for the particulate filter.
The current and upcoming Internal Combustion Engine (ICE) emission norms are very stringent. It is difficult to meet emission standards with just combustion optimization techniques. As a result, post-treatment is required for Engine-out emissions. Otherwise, these hazardous gases impact the ecosystem of living beings. Many technologies are implemented at the exhaust for reducing the emissions. Diesel Particulate Filter (DPF) is one such technique to achieve lower Particulate Matter (PM) and Particulate Number (PN) emission goals. In order to achieve such emission reduction, the DPF undergoes periodic cleaning called regeneration. During regeneration, the exhaust systems including DPF are maintained at elevated temperatures to achieve proper cleaning. When the vehicle is in regeneration, sudden braking or accelerator pedal release leads to engine Drop to Idle speeds (DTI), which sharply increases the temperature gradient inside the DPF which may result in physical damage like cracks, melting and fractures to the DPF substrate. In the occurrence of the above scenario, DPF replacement is the only option which will be an additional expense to the end user. This paper proposes a software solution to address the above issue by detecting the DTI conditions during regeneration and applying corrective actions.
Diesel Particulate Filters (DPFs) have been used extensively worldwide as a Particle Mass (PM) / Particle Number (PN) reduction technology for various diesel applications. Based on CARB’s latest Tier 5 regulation workshop, PM emission targets are expected to become a lot more stringent; from 0.02 g/kWh to 0.005 g/kWh (75% reduction compared to Tier 4 Final (Tier 4f)). Also, CO2 emission targets are expected to be introduced for Tier 5. In parallel, EU Stage VI emission regulation standards and implementation timing could be announced sometime in late 2024. It is expected that PN emission standards will be tightened such as extending measurement range of PN from 23 nm to 10 nm. With Tier 5 and EU Stage VI regulations approaching, several OEMs are considering implementing a common aftertreatment system that can meet emission targets for both regions. High filtration efficiency and low backpressure DPFs will be required to meet PM/PN and CO2 emission standards. NGK has developed several DPF materials with various cell structures within the past couple of years to meet future stringent regulations worldwide. To understand the severity of future regulations, engine testing was conducted to compare PM / PN filtration performance of several different filters. Both Cordierite (Cd) and Silicon Carbide (SiC) materials were evaluated since different materials are being considered depending on soot regeneration strategies. DPF properties ranging from medium to low mean pore size (MPS) were studied and compared with Tier 5 PM / Stage VI PN targets as a reference point. In this paper, the suitable DPF for future regulation based on these results will be shown.
The automobile industry is going through one of the most challenging times, with increased competition in the market which is enforcing competitive prices of the products along with meeting the stringent emission norms. One such requirement for BS6 phase 2 emission norms is monitoring for partial failure of the component if the tailpipe emissions are higher than the OBD limits. Recently PM (soot) sensor is employed for partial failure monitoring of DPF in diesel passenger cars.. PM sensor detects soot leakage in case of DPF substrate failure. There is a cost factor along with extensive calibration efforts which are needed to ensure sensor works flawlessly. This paper deals with the development of an algorithm with which robust detection of DPF substrate failure is achieved without addition of any sensor in the aftertreatment system. In order to achieve this, a thermodynamic model of DPF substate was created using empirical relations between parameters like exhaust flow rate, exhaust gas temperature and soot mass content. The modeling was done in both empty (no soot) and filled (threshold soot content) DPF substrate conditions. There were two methodologies, namely integration method and normalization method. In integration method the pressure drop across DPF substate in actual running condition is cumulatively summed up and compared with the integrated modelled value achieved from the thermodynamic model. If the ratio between modelled and actual crosses the threshold value, a DPF substrate failure flag is raised. In normalization method, actual pressure drop across DPF is corrected using empirical thermodynamic relations for exhaust temperature and soot mass content. The corrected pressure drop is normalized for the exhaust flow rate and then averaged based on release conditions. If the averaged values is lower than the threshold, a DPF substrate failure flag is raised. DPF substrate failure is intimated only when both the methodologies raise the failure flag. The algorithm was tested with actual failed DPF sample and robust detection was observed (more than 90% detection accuracy) and no misdetection.
With the implementation of BS6 Norms, there is an increased focus on reducing particulate matter emissions from gasoline Direct Injection (GDI) engines. GPFs are effective in capturing particulate matter (PM) and particulate number (PN) but their calibration is critical to ensure optimal performance and emissions compliance. This paper presents a study on the calibration of Gasoline Particulate Filters (GPF) to comply with Bharat Stage-6 (BS6) emissions norms. The focus is on thermal management, soot loading, ash loading, and the unique challenges faced in the Indian market. Thermal management strategies include active and passive methods to optimize GPF regeneration and prevent thermal degradation. Soot load detection involves engine-out simulation-based approach as well as delta-Pressure-based approach for accurate soot modelling. Impact of ash loading and its effects on filtration efficiency and pressure drop will also be discussed. Further the strategies to overcome the challenges with respect to variations in driving conditions, ambient conditions, and the need for cost-effective solutions ensuring effective GPF calibration for Indian market.
Recent legislations require very low soot emissions downstream of the particulate filter in diesel vehicles. It will be difficult to meet the new more stringent OBD requirements with standard diagnostic methods based on differential sensors. The use of inexpensive and reliable soot sensors has become the focus of several academic and industrial works over the past decade. In this context, several diagnostic strategies have been developed to detect DPF malfunction based on the soot sensor loading time. This work proposes an advanced online diagnostic method based on soot sensor signal projection. The proposed method is model-free and exclusively uses soot sensor signal without the need for subsystem models or to estimate engine-out soot emissions. It provides a comprehensive and efficient filter monitoring scheme with light calibration efforts. The proposed diagnostic algorithm has been tested on an experimentally validated simulation platform. 2D signatures are generated from soot sensor signal for nominal and faulty configurations. Gaussian dispersions on soot estimator (30%) and sensor model (15%) have been considered. Based on a statistical analysis, a relevant threshold is defined satisfying a compromise between non-detection and false alarm rates. The selected threshold is then used for online DPF diagnostic using NEDC cycle. The obtained results are promising and clearly show the performance of the proposed method in terms of non-detection and false alarm rates. The resulting diagnostic scheme can be easily integrated in the ECU for onboard DPF monitoring.
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