有鑿於全球PM 汙染日益嚴重，於工業及汽車領域，廣泛使用濾煙器(Diesel Particulate Filter, DPF) 來減少PM 之汙染，但隨著時間的增長，DPF 內部累積之PM 會越來越多，而造成排氣背壓的影響，因此，為了避免這種狀況發生，必須在固定的時間點內啟動再生控制，使得PM 能隨著溫度瞬間增加而完全燃燒。
為了要預測DPF 之再生，本論文首先建立了氧化觸媒(Diesel Oxidation Catalytic, DOC)、DPF 以及DPF 後端PIPE 之動態模型，藉由輸入排氣溫度、排氣流量、PM 濃度來預測再生及非再生過程之DOC/DPF/PIPE 內部變化，並利用無跡卡爾曼濾波器(Unscented Kalman Filter, UKF) 建構估測器進行DPF 內部累積PM 質量、DOC 溫度、DPF 溫度及DPF 前後端壓差之狀態估測。
透過DPF 內部累積PM 質量及DPF 前後端壓差狀態之估測，決定DPF 系統何時須啟動及停止再生，並利用估測之DPF 溫度及輸入排氣溫度、流量決定再生時氧化觸媒前端所需之燃油噴注率，使DPF 能在最少的燃油使用量下維持在燃燒PM 所需溫度。有效的利用DPF 過濾功能，降低PM 汙染，利用以上再生控制方法將DPF 內部累積之PM 及DPF 壓差控制在合適的範圍，避免過大的背壓對DPF 前端的引擎性能造成影響。

In view of global PM pollution is becoming serious every day, diesel particulate filter (DPF) has been widely used in In industry and vehicle domain to reduce PM pollution. But as the accumulation time goes by, the PM inside DPF will become more and more. Cause the effect of back pressure, therefore we need to start regeneration in fixed time, make the PM can been burned as the temperature increases.
In order to predict regeneration of DPF, first of all, we build diesel oxidation catalytic (DOC), DPF and Pipe dynamic model. Predict variation inside DOC/DPF/PIPE system during accumulation process and regeneration process via input exhaust temperature, exhaust mass flow rate, PM concentration. And use Unscented Kalman Filter (UKF) for estimating temperature of DOC, temperature of DPF, mass of PM, pressure of downstream DPF, pressure difference of DPF.
Via estimation of PM mass, pressure difference of DPF, decide when to start regeneration and when to stop. Utilizing feature of DPF to reduce PM pollution. Using control strategy of regeneration, make the mass of PM inside DPF and pressure difference of DPF in a proper range. To avoid excessive back pressure effect upstream engine performance.

[1] 蔡瀛逸等, 柴油車氮氧化物、多環芳香烴化合物及粒狀污染物排放減量技術及策略之研究-環保署/國科會空汙防制科研合作計畫. 2012.

[2] Tatsuyuki Kuki, Yukio Miyairi, Yoshiyuki Kasai, Makoto Miyazaki, and Shinichi
Miwa. Study on reliability of wall-flow type diesel particulate filter. mar 2004.

[3] Evdoxia A. Kladopoulou, Song L. Yang, John H. Johnson, Gordon G. Parker, and
Athanasios G. Konstandopoulos. A study describing the performance of diesel par-
ticulate filters during loading and regeneration - a lumped parameter model for control applications. mar 2003.

[4] Pingen Chen and Junmin Wang. Control-oriented modeling and observer-based estimation of solid and gas temperatures for a diesel engine aftertreatment system. Journal of Dynamic Systems, Measurement, and Control, 134(6):061011–061011–12, 2012.

[5] 王衍凱, 以擴張型卡爾曼濾波器為基礎之感應馬達無感測控制及定子轉子阻抗估測, 碩士論文, 台北. 2010.

[6] 李根, 以無跡型卡爾曼濾波器為基礎實現鋰離子電池充電狀態及溫度之即時估測, 國立台灣科技大
學碩士論文, 台北. 2017.

[7] Ltd. taiwan Maruboshi Information Technol-ogy Co. The new generation of diesel engines. May 2007.

[8]“http://air.epa.gov.tw,”行政院環境保護署/細懸浮微粒管制.

[9] Hom N. Sharma, Lakshitha Pahalagedara, Ameya Joshi, Steven L. Suib, and Ashish B.Mhadeshwar. Experimental study of carbon black and diesel engine soot oxidation kinetics using thermogravimetric analysis. Energy & Fuels, 26(9):5613–5625, 2012.

[10] 交通部統計查詢網-機動車輛登記數. 2018.

[11] 全國汽柴油銷售統計-經濟部能源局網站. 2018.

[12] Athanasios G. Konstandopoulos, Margaritis Kostoglou, Evangelos Skaperdas, Eleni Papaioannou, Dimitrios Zarvalis, and Evdoxia Kladopoulou. Fundamental studies of diesel particulate filters: Transient loading, regeneration and aging. mar 2000.

[13] D. Pinturaud, A. Charlet, C. Caillol, P. Higelin, P. Girot, and A. Briot. Experimental study of dpf loading and incomplete regeneration. sep 2007.

[14] Julian C. Tan, Cornelius N. Opris, Kirby J. Baumgard, and John H. Johnson. A study of the regeneration process in diesel particulate traps using a copper fuel additive. feb 1996.

[15] Cuong T. Huynh, John H. Johnson, Song L. Yang, Susan T. Bagley, and James R.
Warner. A one-dimensional computational model for studying the filtration and regeneration characteristics of a catalyzed wall-flow diesel particulate filter. mar 2003.

[16] Pingen Chen and Junmin Wang. Control-oriented model for integrated diesel engine and aftertreatment systems thermal management. Control Engineering Practice, 22:81– 93, 2014.

[17] Ioannis P. Kandylas and Grigorios C. Koltsakis. No2-assisted regeneration of diesel particulate filters: a modeling study. Industrial & Engineering Chemistry Research, 41(9):2115–2123, 2002.

[18] HaralampousO.A.andKoltsakisG.C. Oxygendiffusionmodelingindieselparticulate
filter regeneration. AIChE Journal, 50(9):2008–2019.

[19] Koltsakis Grigorios C. and Stamatelos Anastasios M. Modeling thermal regeneration of wall-flow diesel particulate traps. AIChE Journal, 42(6):1662–1672.

[20] Athanasios G. Konstandopoulos and John H. Johnson. Wall-flow diesel particulate filters—their pressure drop and collection efficiency. feb 1989.

[21] Leonid Tartakovsky, Boris Aronov, and Yoram Zvirin. Modeling of the regeneration processes in diesel particulate filters. 2:96–106, 09 2012.

[22] Keith J. Laidler. The development of the arrhenius equation. Journal of Chemical Education, 61(6):494, 1984.

[23] F. Piscaglia and G. Ferrari. Development of an offline simulation tool to test the on-board diagnostic software for diesel after-treatment systems. apr 2007.

[24] 李宜達, 控制系統設計與模擬, 第 1-1 1-2 頁. 2003.

[25] ”http://www.diesel.com,”diesel net.

[26] Athanasios G. Konstandopoulos and Margaritis Kostoglou. Reciprocating flow regeneration of soot filters. Combustion and Flame, 121(3):488–500, 2000.

[27] Lino Guzzella and Christopher H. Onder. Introduction to modeling and control of internal combustion engine systems. 2004.

[28] G.Bishop G.Welch. An introduction to the kalman filter. 2006.

[29] 程聖傑, 以再生控制策略發展為目的之氧化觸媒/濾煙器後處理系統動態建模, 碩士論文, 台北. 2015.