Hydrogen double compression-expansion engine (H2DCEE): A sustainable internal combustion engine with 60%+ brake thermal efficiency potential at 45 bar BMEP

Rafig Babayev, Hong G. Im, Arne Andersson, Bengt Johansson

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

Hydrogen (H2) internal combustion engines may represent cost-effective and quick solution to the issue of the road transport decarbonization. A major factor limiting their competitiveness relative to fuel cells (FC) is the lower efficiency. The present work aims to demonstrate the feasibility of a H2 engine with FC-like 60%+ brake thermal efficiency (BTE) levels using a double compression-expansion engine (DCEE) concept combined with a high pressure direct injection (HPDI) nonpremixed H2 combustion. Experimentally validated 3D CFD simulations are combined with 1D GT-Power simulations to make the predictions. Several modifications to the system design and operating conditions are systematically implemented and their effects are investigated. Addition of a catalytic burner in the combustor exhaust, insulation of the expander, dehumidification of the EGR, and removal of the intercooling yielded 1.5, 1.3, 0.8, and 0.5%-point BTE improvements, respectively. Raising the peak pressure to 300 bar via a larger compressor further improved the BTE by 1.8%-points but should be accompanied with a higher injector-cylinder differential pressure. The λ of ∼1.4 gave the optimum tradeoff between the mechanical and combustion efficiencies. A peak BTE of 60.3% is reported with H2DCEE, which is ∼5%-points higher than the best diesel-fueled DCEE alternative.
Original languageEnglish (US)
Pages (from-to)115698
JournalENERGY CONVERSION AND MANAGEMENT
Volume264
DOIs
StatePublished - May 19 2022

Bibliographical note

KAUST Repository Item: Exported on 2022-06-17
Acknowledgements: Babayev R. and Johansson B. would like to thank the Combustion Engine Research Center (CERC) at the Chalmers University of Technology for the financial support. Im H.G. was sponsored by the King Abdullah University of Science and Technology (KAUST). The computations and data handling were enabled by resources provided by the Swedish National Infrastructure for Computing (SNIC) at Chalmers Centre for Computational Science and Engineering (C3SE), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. Additionally, Convergent Science provided CONVERGE licenses and technical support for this work.

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