A hybrid spherical fuzzy MCDM framework for optimizing combustion engine performance in automotive engineering
1
College of New Energy Vehicles, Zhengzhou Technical College, Zhengzhou, Henan, 450121, China
2
College of Intelligent Manufacturing, Zhengzhou Technical College, Zhengzhou, Henan, 450121, China
Submission date: 2026-06-05
Final revision date: 2026-08-20
Acceptance date: 2026-09-14
Online publication date: 2026-09-15
Publication date: 2026-09-15
KEYWORDS
Multi-Criteria Decision MakingEngine PerformanceSpherical Fuzzy SetsEmission ReductionCombustion Engine Optimization
TOPICS
ABSTRACT
The increasing demand for sustainable transportation and stringent emission regulations has accelerated the adoption of biodiesel fuels in compression ignition engines. There are several conflicting factors to consider, such as engine performance, combustion properties, fuel economy, and exhaust emissions, when selecting an optimum biodiesel blend, however. Typical optimization methods do not always deal with uncertainty in expert evaluations and the weighting of the objective criterion. The objectives of this study are to develop a new Spherical Fuzzy CRITIC-RAPS (SF-CRITIC-RAPS) method to determine the best blend of Coconut Testa Biodiesel (CTBD) in the combustion engine for improving the engine performance and environmental sustainability.The study was based on the dataset of CTBD Engine Performance, Combustion and Emission Data (2026) available on the Mendeley Data repository. The data set includes experimental data for the various operating conditions, such as Break Thermal Efficiency (BTE), Break Specific Fuel Consumption (BSFC), Break Power, Torque, Cylinder Pressure, Heat Release Rate (HRR), NOx, CO, HC, and Smoke Opacity for the diesel and CTBD blends.
FUNDING
There is no funding.
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| eISSN: | 2449-5220 |
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