Lifecycle optimized ethanol-gasoline blends for turbocharged engines

Bo Zhang, Mani Sarathy

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


This study presents a lifecycle (well-to-wheel) analysis to determine the CO2 emissions associated with ethanol blended gasoline in optimized turbocharged engines. This study provides a more accurate assessment on the best-achievable CO2 emission of ethanol blended gasoline mixtures in future engines. The optimal fuel blend (lowest CO2 emitting fuel) is identified. A range of gasoline fuels is studied, containing different ethanol volume percentages (E0-E40), research octane numbers (RON, 92-105), and octane sensitivities (8.5-15.5). Sugarcane-based and cellulosic ethanol-blended gasolines are shown to be effective in reducing lifecycle CO2 emission, while corn-based ethanol is not as effective. A refinery simulation of production emission was utilized, and combined with vehicle fuel consumption modeling to determine the lifecycle CO2 emissions associated with ethanol-blended gasoline in turbocharged engines. The critical parameters studied, and related to blended fuel lifecycle CO2 emissions, are ethanol content, research octane number, and octane sensitivity. The lowest-emitting blended fuel had an ethanol content of 32 vol%, RON of 105, and octane sensitivity of 15.5; resulting in a CO2 reduction of 7.1%, compared to the reference gasoline fuel and engine technology. The advantage of ethanol addition is greatest on a per unit basis at low concentrations. Finally, this study shows that engine-downsizing technology can yield an additional CO2 reduction of up to 25.5% in a two-stage downsized turbocharged engine burning the optimum sugarcane-based fuel blend. The social cost savings in the USA, from the CO2 reduction, is estimated to be as much as $\$$187 billion/year. © 2016 Elsevier Ltd
Original languageEnglish (US)
Pages (from-to)38-53
Number of pages16
JournalApplied Energy
StatePublished - Aug 16 2016

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: King Abdullah University of Science and Technology


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