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ABSTRACT
Accurate estimation of carbon load in diesel particulate filters is an important basis for efficient and safe operation of active regeneration. Currently, model-based carbon load prediction has the advantages of high accuracy and low influence by working conditions. In this paper, a dynamic model of diesel particulate filters was developed based on the deep bed and cake layer trapping mechanism and regeneration mechanism. The trapping process was described using the spherical trapping mechanism, while the regeneration process was based on the non-catalytic oxidation process of the trapped particles. A pressure drop correction based on the extended Kalman filter was developed to correct the errors accumulated in the carbon load prediction during the integration. The maximum error of carbon load prediction was 0.3 g/L, and the average error was less than 0.17 g/L, the maximum error of pressure drop was 0.7 kPa, and the average error was less than 0.33 kPa. The carbon load prediction algorithm was proved to have good accuracy and can be used for start-stop judgment of DPF active regeneration, and the pressure drop and temperature calculated by the DPF model can be used for DPF status monitoring during regeneration to ensure safety and reliability.
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No potential conflict of interest was reported by the author(s).
Correction Statement
This article has been corrected with minor changes. These changes do not impact the academic content of the article.
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Notes on contributors
Dongwei Yao
Dongwei Yao was born in Deqing, Zhejiang, China, in 1981. He received the B.S. degree in vehicle engineering and the Ph.D. degree in power machinery and engineering from Zhejiang University, Zhejiang, China, in 2005 and 2010, respectively. In 2010, he became an Assistant Professor and also Postdoctoral Fellow with the College of Energy Engineering, Zhejiang University. From 2014 to 2015, he was a Visiting Scholar and Research Assistant with the University of Illinois at Urbana-Champaign, Champaign, IL, USA. Since 2017, he has been an Associate Professor with the College of Energy Engineering, Zhejiang University. He has authored or coauthored more than 30 articles, and attained more than 25 inventions. His research interests include engine combustion and emission control, electrical and hybrid electrical vehicle control.
Jiadong Hu
Jiadong Hu received the B.S. degree in vehicle engineering from Zhejiang University, Zhejiang, China, in 2020. He is currently working toward the Ph.D. degree in power machinery and engineering thermophysics with the College of Energy and Power Engineering, Zhejiang University, Zhejiang, China.
Benxi Zhang
Benxi Zhang received the B.S. degree in vehicle engineering and the Ph.D. degree in power machinery and engineering from Zhejiang University, Zhejiang, China, in 2014 and 2021, respectively.
Yihe Zhang
Yihe Zhang received the B.S. degree in vehicle engineering from Shandong University, Shandong, China, in 2022. He is currently working toward the Ph.D. degree in power machinery and engineering thermophysics with the College of Energy and Power Engineering, Zhejiang University, Zhejiang, China.
Feng Wu
Feng Wu received the B.S. and Ph.D. degrees in internal combustion engine from Zhejiang University, Zhejiang, China, in 1990 and 1996, respectively. In 1996, he became a Lecturer with the Department of Energy Engineering, Zhejiang University. From 1998 to 2006, he was an Associate Professor in the same department. Since 2006, he has been a Full Professor with the College of Energy Engineering, Zhejiang University. He has authored or coauthored more than 50 articles, and attained more than 25 inventions. His research interests include engine combustion and emission control, engine clean alternative fuels, electrical and hybrid electrical vehicle control.