Real-World Vehicle Estimation and Control of Indirect Emissions Control and Performance Evaluation of Electric Vehicles with In-Wheel Motors

dc.AffiliationOctober University for modern sciences and Arts MSA
dc.contributor.authorMohamed S. Shiba
dc.contributor.authorShawki A. Abouel-Seoud
dc.contributor.authorW. Aboelsoud
dc.contributor.authorAhmed S. Abdallah2
dc.date.accessioned2025-10-15T18:20:42Z
dc.date.issued2025-03-17
dc.descriptionSJR 2024 0.281 Q3 H-Index 75
dc.description.abstractThe growing number of automobiles on the road has raised awareness about environmental sustainability and transportation alternatives, sparking ideas about future transportation. Few short-term alternatives meet consumer needs and enable mass production. Because they do not accurately reflect real-world driving. Current models are unable to estimate vehicle emissions. However, the purpose of this research is to present an application of an adaptive neuro-fuzzy inference system for managing the various factors contributing to vehicle gasoline engine exhaust emissions. It examines how well the three known standardized driving cycles (DSCs). Accurately reflect real-world driving and evaluate the impact of real-world driving on vehicle emissions. Indirect emissions are inversely proportional to the vehicle’s fuel consumption. The methodology used is Eco-score methodology to calculate indirect emissions of light vehicles. Expected emission charge estimates for different using styles. Emission rates range substantially between battery classes. The vehicle’s gasoline efficiency is four times better than a similar automobile, but neither mass nor charge multiplied appreciably. The range of this car is not restrained by the battery length, which increases driver comfort, while automobile meets customer expectations in addition to environmental worries and advantages. Despite the fact that they continue to be affordable, they offer a possibility for mass manufacturing reducing overall environmental effects. In keeping with the consequences, the adaptive neuro-fuzzy inference system works nicely to simulate and regulate vehicle engine exhaust emissions. However, the final objective of a regulatory-oriented studies software that focuses on air pollution from mobile sources is to identify and quantify any outcomes that the emissions may have on human fitness. However, before we invest highbrow and economic sources, we need to first recognize the restrictions of modern information and methodologies that preclude accurate estimates of risk to human health. Destiny research packages should be justified by way of their promise to triumph over these boundaries. The goal of this extent, then, is to identify troubles and pick out a studies schedule with a purpose to be only in advancing our potential to quantify the fitness dangers related to air pollution.
dc.description.urihttps://www.scimagojr.com/journalsearch.php?q=19700174711&tip=sid&clean=0
dc.identifier.citationShiba, M. S., Abouel-Seoud, S. A., Aboelsoud, W., & Abdallah, A. S. (2025). Real-World Vehicle Estimation and Control of Indirect Emissions Control and Performance Evaluation of Electric Vehicles with In-Wheel Motors. SAE International Journal of Engines, 18(2). https://doi.org/10.4271/03-18-02-0016
dc.identifier.doihttps://doi.org/10.4271/03-18-02-0016
dc.identifier.otherhttps://doi.org/10.4271/03-18-02-0016
dc.identifier.urihttps://repository.msa.edu.eg/handle/123456789/6554
dc.language.isoen_US
dc.publisherSAE International
dc.relation.ispartofseriesSAE International Journal of Engines ; Volume: 18 , Issue Number: 2
dc.subjectElectric vehicle
dc.subjectFuel consumption
dc.subjectEnergy consumption
dc.subjectIndirect emissions
dc.subjectECE 15 (urban driving cycle)
dc.subjectUS06 (city mode driving cycle)
dc.titleReal-World Vehicle Estimation and Control of Indirect Emissions Control and Performance Evaluation of Electric Vehicles with In-Wheel Motors
dc.typeArticle
eperson.orcidDr. Mohamed S. Shiba
eperson.orcidhttps://orcid.org/0000-0003-1969-9440

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