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Assessing fuel economy and NOx emissions of a hydrogen engine bus using neural network algorithms for urban mass transit systems

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  • Kim, Seongsu
  • Kim, Junghwan

Abstract

The transition from compressed natural gas (CNG) to hydrogen has begun in mass transportation applications in Seoul, South Korea. This study investigates the feasibility of using a hydrogen combustion engine for city buses in Seoul. A hydrogen-fueled, six-cylinder, 11,046-cm3 spark-ignition engine equipped with a mixer-type fuel supply system is proposed. An experiment using a single-cylinder engine is performed to obtain operating and performance maps. These maps are then used in the vehicle simulation model. Combustion characteristics are investigated using three-dimensional numerical simulation validated by the experimental results. A regression analysis is conducted using neural network algorithms to determine the dominant operating parameters on nitric oxides (NOx) emissions, and 373 bus routes in Seoul are analyzed using real-time driving data and recent annual statistics. The vehicle driving simulation using actual Seoul bus driving data reveals an average fleet fuel economy of 121.7 g/km, confirming that hydrogen-engine buses can be more efficient than the CNG buses currently in use. A 70-MPa tank can store 34.78 kg of hydrogen, which yields a maximum travel distance of 388 km longer than the longest route (bus #9411 at 77 km). The result indicates that even a 20-Mpa fuel tank, enabling a bus to travel 144 km, is sufficient for Seoul city buses.

Suggested Citation

  • Kim, Seongsu & Kim, Junghwan, 2023. "Assessing fuel economy and NOx emissions of a hydrogen engine bus using neural network algorithms for urban mass transit systems," Energy, Elsevier, vol. 275(C).
    • Handle: RePEc:eee:energy:v:275:y:2023:i:c:s0360544223009118
    DOI: 10.1016/j.energy.2023.127517
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    References listed on IDEAS

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    1. Richard H. R. Hahnloser & Rahul Sarpeshkar & Misha A. Mahowald & Rodney J. Douglas & H. Sebastian Seung, 2000. "Correction: Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, Nature, vol. 408(6815), pages 1012-1012, December.
    2. Liu, Jinlong & Dumitrescu, Cosmin E., 2019. "Single and double Wiebe function combustion model for a heavy-duty diesel engine retrofitted to natural-gas spark-ignition," Applied Energy, Elsevier, vol. 248(C), pages 95-103.
    3. Richard H. R. Hahnloser & Rahul Sarpeshkar & Misha A. Mahowald & Rodney J. Douglas & H. Sebastian Seung, 2000. "Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit," Nature, Nature, vol. 405(6789), pages 947-951, June.
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    Cited by:

    1. Zhihong Wang & Kai Feng, 2024. "NOx Emission Prediction for Heavy-Duty Diesel Vehicles Based on Improved GWO-BP Neural Network," Energies, MDPI, vol. 17(2), pages 1-23, January.

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