Accurately obtaining the soot loading in the Diesel particulate filter (DPF) is important for DPF, and current online real-time estimation methods only go up to about 2 g/L. In this paper, the DPF structure is simplified into a gas-capacity combined with gas-resistance, and the theoretical analysis of the simplified structure is carried out through the equivalent circuit analysis method. The time constant corresponding to the DPF during the dynamic working state of the diesel engine is solved, and further analysis reveals that it is related to the incoming flow temperature T and the soot loading . On this basis, a method for estimating the soot loading based on the time constant is proposed. Subsequently, experiments on engine load characteristics at different speed working states were carried out, and the dynamic process of DPF was analyzed at different soot loading . It was found that the time constant can be characterized by the difference in temperature stabilization time between the inlet and outlet; The smaller the incoming temperature T, the more significant the relationship between the soot loading and the time constant ; The proposed estimation method allows for the identification of soot loading down to 1 g/L.
JianSYangYRenW, et al. Kinetic analysis of morphologies and crystal planes of nanostructured CeO2 catalysts on soot oxidation. Chem Eng Sci2020; 226(23): 115891.
2.
MengZChenCLiJ, et al. Particle emission characteristics of DPF regeneration from DPF regeneration bench and diesel engine bench measurements. Fuel2020; 262(15): 116589.
3.
MengZLiJFangJ, et al. Experimental study on regeneration performance and particle emission characteristics of DPF with different inlet transition sections lengths. Fuel2020; 262(15): 116487.
4.
CuiBZhouLLiK, et al. Holey Co-Ce oxide nanosheets as a highly efficient catalyst for diesel soot combustion. Appl Catal B2020; 267(15): 118670.
5.
BensaidSMarchisioDLFinoD, et al. Modelling of diesel particulate filtration in wall-flow traps. Chem Eng J2009; 154(1–3): 211–218.
6.
ChoiSOhKCLeeCB. The effects of filter porosity and flow conditions on soot deposition/oxidation and pressure drop in particulate filters. Energy2014; 77(1): 327–337.
7.
PuXCaiYShiY, et al. Diesel particulate filter (DPF) regeneration using non-thermal plasma induced by dielectric barrier discharge. J Energy Inst2018; 91(5): 655–667.
8.
DittlerA. The application of diesel particle filters—from past to present and beyond. Top Catal2017; 60(3–5): 342–347.
9.
TanPQDuanLSLiEF, et al. Experimental study on the temperature characteristics of a diesel particulate filter during a drop to idle active regeneration process. Appl Therm Eng2020; 178: 115628.
10.
ZhangBJiaqiangEGongJ, et al. Multidisciplinary design optimization of the diesel particulate filter in the composite regeneration process. Appl Energy2016; 181: 14–28.
11.
KurienCSrivastavaAKGandigudiN, et al. Soot deposition effects and microwave regeneration modelling of diesel particulate filtration system. J Energy Inst2020; 93(2): 463–473.
12.
LaoCTAkroydJEavesN, et al. Modelling particle mass and particle number emissions during the active regeneration of diesel particulate filters. Proc Combust Inst2019; 37(4): 4831–4838.
13.
RanYHuangTZhangM, et al. DPF soot loading estimation strategy based on pressure difference. IFAC-PapersOnLine2018; 51(31): 366–368.
14.
BaiSTangJWangG, et al. Soot loading estimation model and passive regeneration characteristics of DPF system for heavy-duty engine. Appl Therm Eng2016; 100: 1292–1298.
15.
BissettEJ. Mathematical model of the thermal regeneration of a wall-flow monolith diesel particulate filter. Chem Eng Sci1984; 39(7–8): 1233–1244.
16.
OprisCNJohnsonJH. A 2-D computational model describing the flow and filtration characteristics of a ceramic diesel particulate trap. SAE technical paper980545, 1998.
17.
DabhoiwalaRHJohnsonJHNaberJD. A methodology to estimate the mass of particulate matter retained in a catalyzed particulate filter as applied to active regeneration and on-board diagnostics to detect filter failures. SAE technical paper 2008-01-0764, 2008.
18.
DengYZhengWJiaqiangE, et al. Influence of geometric characteristics of a diesel particulate filter on its behavior in equilibrium state. Appl Therm Eng2017; 123: 61–73.
19.
KongXLiZLiangX. Simulating the flow and soot loading in wall-flow DPF using a two-dimensional mesoscopic model. SAE technical paper 2018-01-0955, 2018.
20.
YoonCSSongSHChunKM. Measurement of soot mass and pressure drop using a single channel DPF to determine soot permeability and density in the wall flow filter. SAE technical paper 2007-01-0311, 2007.
21.
OrihuelaMPChacarteguiRGómez-MartínA, et al. Performance trends in wall-flow diesel particulate filters: comparative analysis of their filtration efficiency and pressure drop. J Clean Prod2020; 260: 120863.
22.
SinghNMandarapuS. DPF soot estimation challenges and mitigation strategies and assessment of available DPF technologies. SAE technical paper 2013-01-0838, 2013.
23.
ShenYBHaradaTTeranishiS, et al. A solid-state particulate matter sensor based on electrochemical oxidation of carbon by active oxygen. Sens Actuators B2012; 162(1): 159–165.
24.
ShenYBTakeuchiTHibinoT. Design of a highly sensitive and responsive electrode for particulate matter monitoring. Sens Actuators B Chem2011; 153(1): 226–231.
25.
HagenGFeulnerMWernerR, et al. Capacitive soot sensor for diesel exhausts. Sens Actuators B2016; 236: 1020–1027.
26.
KondoAYokoiSSakuraiT, et al. New particulate matter sensor for on board diagnosis. SAE Int J Engines2011; 4(1): 117–125.
27.
OchsTSchittenhelmHGenssleA, et al. Particulate matter sensor for on board diagnostics (OBD) of diesel particulate filters (DPF). SAE Int J Fuels Lubr2010; 3(1): 61–69.
28.
ShenYTakeuchiTTeranishiS, et al. Alumina substrate-supported electrochemical device for potential application as a diesel particulate matter sensor. Sens Actuators B Chem2010; 145(2): 708–712.
29.
BilbyDKubinskiDJMaricqMM. Current amplification in an electrostatic trap by soot dendrite growth and fragmentation: application to soot sensors. J Aerosol Sci2016; 98: 41–58.
30.
MaricqMMBilbyD. The impact of voltage and flow on the electrostatic soot sensor and the implications for its use as a diesel particulate filter monitor. J Aerosol Sci2018; 124: 41–53.
31.
RoseDBogerT. Different approaches to soot estimation as key requirement for DPF applications. SAE technical paper 2009-01-1262, 2009.
32.
MandatoriPMGülderÖL. Soot formation in laminar ethane diffusion flames at pressures from 0.2 to 3.3MPa. Proc Combust Inst2011; 33(1): 577–584.
33.
ZhaoFYangWYuW. A progress review of practical soot modelling development in diesel engine combustion. J Traffic Transp Eng2020; 7(3): 269–281.
34.
KurienC. Review on post-treatment emission control technique by application of diesel oxidation catalysis and diesel particulate filtration. J Therm Eng2019; 5: 108–118.
35.
RodriguezSRomeroCSargentiML, et al. Flow of polymer solutions through porous media. J Nonnewton Fluid Mech1993; 49(1): 63–85.
36.
KonstandopoulosAGKostoglouMSkaperdasE, et al. Fundamental studies of diesel particulate filters: transient loading, regeneration and aging. In: SAE 2000 world congress, Detroit, MI, USA, 2000, p.25.
37.
OgyuKYamakawaTIshiiY, et al. Soot loading estimation accuracy improvement by filtration layer forming on DPF and new algorithm of pressure loss measurement. SAE technical paper 2013-01-0525, 2013.
38.
GalindoJSerranoJRPiquerasP, et al. Heat transfer modelling in honeycomb wall-flow diesel particulate filters. Energy2012; 43(1): 201–213.
