Estimation of DPF Soot Loading through Steady-State Engine Mapping and Simulation for Automotive Diesel Engines Running on Petroleum-Based Fuels

Barba, Francesco; Vassallo, Alberto; Greco, V.

doi:10.4271/2017-24-0139

Cited by 3 publications

(2 citation statements)

References 16 publications

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“…In operation, maintenance action (i.e. regeneration) is needed once the DPF State 3 is reached, and research efforts are targeted to deliver further system optimisation to avoid unnecessary DPF regenerations due to "under-loading" (Barba et al, 2017). For example, a recent study from General Motors presented a methodology focussing on a more reliable engine-out soot mass simulation approach (Barba et al, 2017).…”

Section: Reliability Based Paradigm For Dpf Operational Managementmentioning

confidence: 99%

“…regeneration) is needed once the DPF State 3 is reached, and research efforts are targeted to deliver further system optimisation to avoid unnecessary DPF regenerations due to "under-loading" (Barba et al, 2017). For example, a recent study from General Motors presented a methodology focussing on a more reliable engine-out soot mass simulation approach (Barba et al, 2017). Dawei et al (2017) proposed a new method for DPF regeneration, underpinned by mathematical model for more accurate regeneration timing and temperature control.…”

Section: Reliability Based Paradigm For Dpf Operational Managementmentioning

confidence: 99%

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A Reliability Inspired Strategy for Intelligent Performance Management With Predictive Driver Behaviour: A Case Study for a Diesel Particulate Filter

et al. 2021

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The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle performance, including compliance with stringent legislation. This paper illustrates this approach with a case study for a Diesel Particulate Filter, where machine learning deployed to real world automotive data is used in conjunction with a reliability inspired performance modelling paradigm to design a strategy to enhance operational performance based on predictive driver behaviour. The model-in-the-loop simulation of the proposed strategy on a fleet of vehicles showed significant improvement compared to the base strategy, demonstrating the value of the approach.

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Section: Reliability Based Paradigm For Dpf Operational Managementmentioning

confidence: 99%

Section: Reliability Based Paradigm For Dpf Operational Managementmentioning

confidence: 99%

A Reliability Inspired Strategy for Intelligent Performance Management With Predictive Driver Behaviour: A Case Study for a Diesel Particulate Filter

et al. 2021

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Experimental and Computational Investigation of Particle Filtration Mechanisms in Partially Damaged DPFs

Haralampous¹,

Μαστρόκαλος²,

Tzorbatzoglou³

2019

SAE Int. J. Adv. & Curr. Prac. in Mobility

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<div class="section abstract"><div class="htmlview paragraph">Understanding the filtration mechanisms in partially damaged Diesel Particulate Filters is very important for the design of exhaust systems with efficient On-Board Diagnosis functionality, especially as new threshold limits have been recently applied for particulate mass leakage. Two common types of DPF failure are included in this study, namely rear plug removal and internal failure due to uncontrolled regeneration with excessive deposit loading. Initially, the two respective filters were loaded on the engine bench with particle measurement upstream and downstream, and then they were disassembled and sectioned to study the deposit distribution. The analysis of the second filter revealed several modes of failure that should be expected under real-life conditions such as material accumulation in the inlet channels, substrate melting, and crosswise and diagonal crack development. Moreover, a computational model with the necessary adjustments is used to simulate the loading experiments and interpret the underlying filtration mechanisms. The processed results reveal small effects of temperature and mass flow rate on the filtration efficiency and a comparatively stronger impact of the total deposit loading. The local deposit loading is uniform in the intact segments, while it is non-uniform with a minimum value at the failure location in the unplugged and internally damaged segments. This finding is consistent with the wall flow predicted by the model, whereas some discrepancies of the model can be attributed to a secondary collection mechanism.</div></div>

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A Model Based Approach to DPF Soot Estimation and Validation for BSVI Commercial Vehicles in Context to Indian Driving Cycles

Jain

Singh

Kardile

et al. 2021

SAE Technical Paper Series

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Estimation of DPF Soot Loading through Steady-State Engine Mapping and Simulation for Automotive Diesel Engines Running on Petroleum-Based Fuels

Cited by 3 publications

References 16 publications

A Reliability Inspired Strategy for Intelligent Performance Management With Predictive Driver Behaviour: A Case Study for a Diesel Particulate Filter

A Reliability Inspired Strategy for Intelligent Performance Management With Predictive Driver Behaviour: A Case Study for a Diesel Particulate Filter

Experimental and Computational Investigation of Particle Filtration Mechanisms in Partially Damaged DPFs

A Model Based Approach to DPF Soot Estimation and Validation for BSVI Commercial Vehicles in Context to Indian Driving Cycles

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