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Reactivity controlled compression ignition engine: Pathways towards commercial viability

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  • Paykani, Amin
  • Garcia, Antonio
  • Shahbakhti, Mahdi
  • Rahnama, Pourya
  • Reitz, Rolf D.

Abstract

Reactivity-controlled compression ignition (RCCI) is a promising energy conversion strategy to increase fuel efficiency and reduce nitrogen oxide (NOx) and soot emissions through improved in-cylinder combustion process. Considering the significant amount of conducted research and development on RCCI concept, the majority of the work has been performed under steady-state conditions. However, most thermal propulsion systems in transportation applications require operation under transient conditions. In the RCCI concept, it is crucial to investigate transient behavior over entire load conditions in order to minimize the engine-out emissions and meet new real driving emissions (RDE) legislation. This would help further close the gap between steady-state and transient operation in order to implement the RCCI concept into mass production. This work provides a comprehensive review of the performance and emissions analyses of the RCCI engines with the consideration of transient effects and vehicular applications. For this purpose, various simulation and experimental studies have been reviewed implementing different control strategies like control-oriented models particularly in dual-mode operating conditions. In addition, the application of the RCCI strategy in hybrid electric vehicle platforms using renewable fuels is also discussed. The discussion of the present review paper provides important insights for future research on the RCCI concept as a commercially viable energy conversion strategy for automotive applications.

Suggested Citation

  • Paykani, Amin & Garcia, Antonio & Shahbakhti, Mahdi & Rahnama, Pourya & Reitz, Rolf D., 2021. "Reactivity controlled compression ignition engine: Pathways towards commercial viability," Applied Energy, Elsevier, vol. 282(PA).
    • Handle: RePEc:eee:appene:v:282:y:2021:i:pa:s0306261920315786
    DOI: 10.1016/j.apenergy.2020.116174
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    2. Zhang, Hao & Fan, Qinhao & Liu, Shang & Li, Shengbo Eben & Huang, Jin & Wang, Zhi, 2021. "Hierarchical energy management strategy for plug-in hybrid electric powertrain integrated with dual-mode combustion engine," Applied Energy, Elsevier, vol. 304(C).
    3. Wu, Jingtao & Zhang, Zhehao & Kang, Zhe & Deng, Jun & Li, Liguang & Wu, Zhijun, 2022. "An assessment methodology for fuel/water consumption co-optimization of a gasoline engine with port water injection," Applied Energy, Elsevier, vol. 310(C).
    4. Jan Verhaegh & Frank Kupper & Frank Willems, 2022. "Data-Driven Air-Fuel Path Control Design for Robust RCCI Engine Operation," Energies, MDPI, vol. 15(6), pages 1-25, March.
    5. Abhinandhan Narayanan & Deivanayagam Hariharan & Kendyl Ryan Partridge & Austin Leo Pearson & Kalyan Kumar Srinivasan & Sundar Rajan Krishnan, 2023. "Impact of Low Reactivity Fuel Type and Energy Substitution on Dual Fuel Combustion at Different Injection Timings," Energies, MDPI, vol. 16(4), pages 1-36, February.
    6. Giacomo Silvagni & Abhinandhan Narayanan & Vittorio Ravaglioli & Kalyan Kumar Srinivasan & Sundar Rajan Krishnan & Nik Collins & Paulius Puzinauskas & Fabrizio Ponti, 2023. "Experimental Characterization of Hydrocarbons and Nitrogen Oxides Production in a Heavy-Duty Diesel–Natural Gas Reactivity-Controlled Compression Ignition Engine," Energies, MDPI, vol. 16(13), pages 1-19, July.
    7. Asish K. Sarangi & Gordon P. McTaggart-Cowan & Colin P. Garner, 2022. "The Impact of Fuel Injection Timing and Charge Dilution Rate on Low Temperature Combustion in a Compression Ignition Engine," Energies, MDPI, vol. 16(1), pages 1-21, December.
    8. Maarten Vlaswinkel & Frank Willems, 2024. "Data-Based In-Cylinder Pressure Model with Cyclic Variations for Combustion Control: An RCCI Engine Application," Energies, MDPI, vol. 17(8), pages 1-19, April.
    9. García, Antonio & Monsalve-Serrano, Javier & Lago Sari, Rafael & Tripathi, Shashwat, 2022. "Pathways to achieve future CO2 emission reduction targets for bus transit networks," Energy, Elsevier, vol. 244(PB).
    10. He, Hongwen & Meng, Xiangfei & Wang, Yong & Khajepour, Amir & An, Xiaowen & Wang, Renguang & Sun, Fengchun, 2024. "Deep reinforcement learning based energy management strategies for electrified vehicles: Recent advances and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    11. Park, Hyunwook & Shim, Euijoon & Lee, Junsun & Oh, Seungmook & Kim, Changup & Lee, Yonggyu & Kang, Kernyong, 2023. "Comparative evaluation of conventional dual fuel, early pilot, and reactivity-controlled compression ignition modes in a natural gas-diesel dual-fuel engine," Energy, Elsevier, vol. 268(C).

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