Publication Type : Journal Article
Publisher : Informa UK Limited
Source : International Journal of Ambient Energy
Url : https://doi.org/10.1080/01430750.2026.2671263
Keywords : Alkaline water electrolyser, prediction model, engine performance and emissions, ethanol–gasoline blend, hydrogen enrichment,
Campus : Coimbatore
School : School of Engineering
Department : Mechanical Engineering
Year : 2026
Abstract : This study experimentally and numerically investigates hydrogen enrichment of E30 fuel (30% ethanol + 70% gasoline) using an onboard alkaline electrolyser in a spark-ignition engine. A compact stainless-steel (SS-304L) electrolyser with a 2600 mm2 active plate area generated hydrogen at 0.5–2.0 LPM, regulated through load-based current control. Engine tests were conducted at five different load conditions under steady-state operation. Compared with baseline E30 operation, hydrogen enrichment resulted in accelerated combustion, characterised by an increase in peak in-cylinder pressure from 77.1 to 84.05 bar and a rise in peak heat release rate from 57.6 to 68 J/°CA at full load, indicating faster flame propagation and reduced combustion duration. Consequently, brake thermal efficiency improved from 30.17% to 33% at full load, reaching a maximum of 34.8% at 75% load, while brake specific fuel consumption decreased from 0.31 to 0.25 kg/kWh. Carbon monoxide, hydrocarbons, and methane emissions were significantly reduced, whereas nitrogen oxides increased moderately due to elevated combustion temperatures. A feed-forward artificial neural network was developed to predict engine performance and emission responses by capturing the nonlinear interaction between hydrogen enrichment and engine load. The optimised model achieved high predictive accuracy (R2 ≈ 0.9998) demonstrating strong potential low-carbon spark-ignition engine operation.
Cite this Research Publication : Sivanesan Murugesan, Srihari S, Senthilkumar Duraisamy, Praveenkumar Thangavelu, Onboard electrolyser-based hydrogen enrichment of E30 fuel for SI engines: experimental and machine learning-assisted optimisation, International Journal of Ambient Energy, Informa UK Limited, 2026, https://doi.org/10.1080/01430750.2026.2671263