Investigating the effect of E30 fuel on long term vehicle performance, adaptability and economic feasibility

被引:6
作者
Alsiyabi, Adil [1 ]
Stroh, Seth [1 ]
Saha, Rajib [1 ]
机构
[1] Univ Nebraska Lincoln, Dept Chem & Biomol Engn, Lincoln, NE USA
关键词
Higher ethanol blend fuel; Vehicle performance; Machine learning; Economic analysis; UNLEADED GASOLINE BLENDS; ENGINE PERFORMANCE; POLLUTANT EMISSION; ETHANOL;
D O I
10.1016/j.fuel.2021.121629
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Due to the drawbacks associated with the use of petroleum derived fuels, the use of more sustainable fuel sources has garnered increasing attention in several sectors including road transportation. However, the transition away from gasoline is often hindered by the inability of currently operating vehicles to efficiently run under alternative fuels. Therefore, the logical short-term alternative is to transition to clean fuel sources including higher-ethanol fuel blends that are compatible with current fuel systems and spark-ignition engines. In this work, the long-term adaptability and economic feasibility of non-flex vehicles to consume a 30% ethanol (E30) fuel blend was investigated. Sixteen diagnostic and operating parameters were tracked in real-time in 50 vehicles over a oneyear period. The vast amount of data generated was used to train sparse regression and machine learning models to explore differences in performance and operational robustness of commercial vehicles consuming E30 blends compared to the ones consuming 15% blends. Results indicate that although modest changes can be observed in the behavior of a subset of parameters, overall performance and adaptability are not compromised by consumption of E30. It was determined that an average price difference of 2.5% is sufficient to offset the mileage loss caused by the increased ethanol concentration. Finally, we discuss the large-scale environmental impact of an incremental nation-wide shift towards E30 consumption.
引用
收藏
页数:8
相关论文
共 30 条
[1]   State-of-the-art in artificial neural network applications: A survey [J].
Abiodun, Oludare Isaac ;
Jantan, Aman ;
Omolara, Abiodun Esther ;
Dada, Kemi Victoria ;
Mohamed, Nachaat AbdElatif ;
Arshad, Humaira .
HELIYON, 2018, 4 (11)
[2]  
Al-Hasan A, 2003, ENERG CONVERS MANAGE, V44, P1547
[3]  
[Anonymous], Annual Energy Outlook 2019: New Light Duty Vehicle Prices
[4]   Discovering governing equations from data by sparse identification of nonlinear dynamical systems [J].
Brunton, Steven L. ;
Proctor, Joshua L. ;
Kutz, J. Nathan .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2016, 113 (15) :3932-3937
[5]  
Çelik MB, 2008, J FAC ENG ARCHIT GAZ, V23, P619
[6]   Cold-start emissions of an SI engine using ethanol-gasoline blended fuel [J].
Chen, Rong-Horng ;
Chiang, Li-Bin ;
Chen, Chung-Nan ;
Lin, Ta-Hui .
APPLIED THERMAL ENGINEERING, 2011, 31 (8-9) :1463-1467
[7]  
Dash S.S., 2021, REV MACHINE LEARNING, P495
[8]  
DuMont R.J., 2009, SAE Technical Paper Series, DOI DOI 10.4271/2009-01-2641
[9]  
EIA, 2016, ALM ALL US GAS BLEND
[10]  
Environmental Protection Agency, 2018, GREENHOUSE GAS EMISS