本文關(guān)鍵詞：基于模型的柴油機(jī)微粒捕集器碳煙加載量估計(jì)研究　出處：《吉林大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 柴油機(jī)微粒捕集器 零維集總參數(shù) 碳煙加載量 估計(jì) 被動(dòng)再生

【摘要】：汽車工業(yè)的可持續(xù)發(fā)展面臨著能源與環(huán)境的雙重挑戰(zhàn)。日益頻發(fā)的大氣霧霾和日益嚴(yán)厲的排放法規(guī)，對柴油機(jī)的碳煙排放提出了嚴(yán)格要求，迫切需要通過“機(jī)外”后處理系統(tǒng)實(shí)現(xiàn)對柴油機(jī)碳煙的進(jìn)一步凈化。柴油機(jī)微粒捕集器（DPF）是目前公認(rèn)的降低碳煙排放最有效的技術(shù)之一。為了降低由于DPF內(nèi)碳煙持續(xù)加載引起的背壓升高對柴油機(jī)經(jīng)濟(jì)性和動(dòng)力性的影響，需要定期進(jìn)行再生。DPF再生面臨著再生不完全和不安全（DPF載體燒熔和破裂等）的問題，準(zhǔn)確判斷DPF再生時(shí)機(jī)成為解決該問題的關(guān)鍵。而準(zhǔn)確判斷再生時(shí)機(jī)實(shí)質(zhì)是需要精確估計(jì)DPF的碳煙加載量。傳統(tǒng)的碳煙加載量估計(jì)方法估計(jì)精度有待進(jìn)一步提高，且實(shí)時(shí)應(yīng)用困難。因此，本文基于MATLAB/Simulink平臺建立了適用于ECU實(shí)時(shí)計(jì)算的DPF碳煙加載模型，開展了基于模型的DPF碳煙加載量估計(jì)研究。 針對DPF一維三維建模復(fù)雜的特點(diǎn)，本文建立了基于填充床捕集理論和零維集總參數(shù)方法的碳煙加載模型。首先，依據(jù)填充床捕集理論對DPF載體壁面進(jìn)行離散分層，基于布朗擴(kuò)散沉積機(jī)理和直接攔截沉積機(jī)理建立了DPF壁面加載模型。然后，引入分配系數(shù)，實(shí)現(xiàn)碳煙加載質(zhì)量在碳煙層和壁面內(nèi)的分配，從而實(shí)現(xiàn)對不考慮被動(dòng)再生的碳煙加載過程的建模。最后，結(jié)合DPF碳煙加載過程可能發(fā)生的宏觀多相化學(xué)反應(yīng)（NO2輔助氧化等），依據(jù)各反應(yīng)的起始作用溫度，建立了四種模式的DPF零維集總參數(shù)質(zhì)量、能量平衡模型，實(shí)現(xiàn)考慮被動(dòng)再生時(shí)DPF加載過程碳煙層和壁面內(nèi)碳煙質(zhì)量更新的建模。從而建立了考慮被動(dòng)再生時(shí)DPF碳煙加載估計(jì)模型。 針對DPF碳煙加載估計(jì)模型控制參數(shù)，提出了基于線性擬合和最小二乘法的模型參數(shù)辨識方法�；谠囼�(yàn)數(shù)據(jù)，實(shí)現(xiàn)了對所選穩(wěn)態(tài)加載工況下的DPF捕集參數(shù)和化學(xué)動(dòng)力學(xué)參數(shù)的辨識。 通過柴油機(jī)臺架試驗(yàn)對DPF碳煙加載估計(jì)模型進(jìn)行了驗(yàn)證并對DPF碳煙加載估計(jì)模型的仿真結(jié)果進(jìn)行了分析。驗(yàn)證試驗(yàn)包括后處理系統(tǒng)只加裝DPF和加裝DOC+DPF兩組試驗(yàn)。每組試驗(yàn)分別進(jìn)行潔凈DPF壓降試驗(yàn)和DPF穩(wěn)態(tài)加載試驗(yàn)。通過比較模型預(yù)測的潔凈DPF壓降、DPF穩(wěn)態(tài)加載壓降特性與試驗(yàn)測得的潔凈DPF壓降、DPF穩(wěn)態(tài)加載壓降特性，間接對DPF碳煙加載估計(jì)模型進(jìn)行驗(yàn)證。結(jié)果表明，模型對潔凈DPF壓降和DPF穩(wěn)態(tài)加載壓降特性的預(yù)測與試驗(yàn)結(jié)果有很好的吻合性。 基于以上提出的DPF碳煙加載估計(jì)模型，運(yùn)用Simulink仿真軟件對其進(jìn)行了仿真研究。仿真結(jié)果表明，基于該模型的DPF碳煙加載量估計(jì)，由于考慮了被動(dòng)再生對碳煙加載量的影響，，碳煙加載量的估計(jì)精度得到提高。并且該模型很好的實(shí)現(xiàn)了對DPF碳煙加載過程不同時(shí)刻的碳煙加載量和壓降特性的跟蹤。DPF碳煙加載量估計(jì)數(shù)學(xué)模型的建立，為進(jìn)一步實(shí)現(xiàn)基于模型的DPF再生控制策略的開發(fā)打下了基礎(chǔ)。
[Abstract]:The sustainable development of the automobile industry is facing dual challenges of energy and environment. The increasingly frequent haze and increasingly stringent emission regulations, put forward strict requirements on smoke emission of diesel engine, the urgent need for the adoption of "machine" system to achieve the postprocessing for further purification of diesel soot of diesel particulate filter. Device (DPF) is now recognized as one of the most effective techniques to reduce soot emissions. In order to decrease due to the increase of soot in DPF continuous loading caused by backpressure on the fuel economy and power, the need for regular regeneration.DPF regeneration facing regeneration is incomplete and not safe (DPF vector melting and rupture etc.) problems, accurately determine the DPF regeneration time becomes the key to solve the problem. And accurately determine the regeneration time is need to accurately estimate soot loading DPF soot loading. The traditional estimation method to estimate the precision It needs further improvement and real-time application is difficult. Therefore, based on MATLAB/Simulink platform, a DPF soot loading model suitable for ECU real-time computation is established, and DPF based soot loading estimation is carried out based on the model.
According to the characteristics of DPF one-dimensional complex 3D modeling, this paper established the soot loading model of packed bed trapping theory and zero dimensional lumped parameter method based on. Firstly, according to the packed bed trapping theory of DPF vector discrete wall stratification, Brown diffusion and deposition mechanism of direct interception sedimentation mechanism established DPF wall loading based on the model. Then, the distribution coefficient, realize the distribution of soot loading quality in the soot layer and the wall surface, thus realizing the modeling without considering the soot loading process of passive regeneration. Finally, combining the macro DPF soot loading process may occur by chemical reaction (NO2 assisted oxidation), on the basis of effect of starting temperature of the reaction, set up four kinds of mode DPF and zero dimensional lumped mass and energy balance model, modeling update soot mass considering passive regeneration DPF loading process of soot layer and inside the wall. In order to establish The DPF carbon smoke loading estimation model is considered when passive regeneration is used.
Aiming at the control parameters of DPF soot loading estimation model, a model identification method based on linear fitting and least square method is proposed. Based on the experimental data, the DPF trapping parameters and chemical kinetic parameters of the selected steady-state loading conditions are identified.
The engine bench test of DPF soot loading estimation model was verified on DPF and soot loading estimation model simulation results are analyzed. Experiments including postprocessing system only adding DPF and adding DOC+DPF two group test. Each test were clean DPF drop test and DPF test. Through static loading clean DPF drop the comparison of model predictions, clean DPF DPF drop steady loading pressure drop characteristics with the measured DPF, steady-state load pressure drop characteristics, indirect DPF soot loading estimation model was verified. The results show that the model of clean DPF pressure drop and pressure drop characteristics of DPF steady-state load prediction and the experimental results have a good agreement.
Estimation model based on soot loading DPF proposed above, the use of Simulink simulation software is simulated. The simulation results show that the DPF estimation of soot loading based on the model, considering the influence of passive regeneration of soot loading, the soot loading estimation precision level improved and the model. To achieve a good tracking.DPF soot loading on soot loading and pressure drop characteristics of DPF soot loading process in different time to set up the mathematical model for the further implementation of the estimation model of DPF based on the development of regeneration control strategy to lay the foundation.

【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TK421.5

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