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Energy management strategies comparison for a parallel full hybrid electric vehicle using Reactivity Controlled Compression Ignition combustion

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  • García, Antonio
  • Carlucci, Paolo
  • Monsalve-Serrano, Javier
  • Valletta, Andrea
  • Martínez-Boggio, Santiago

Abstract

Reactivity Controlled Compression Ignition combustion technology potentials are well known for the capability to drastically reduce the engine-out nitrogen oxides and soot emissions simultaneously. Its implementation in mid-term low-duty diesel engines can be beneficial to meet the upcoming regulations. To explore the potential of this solution, experimental data are used from a compression ignition 1.9 L engine, which is operated under two combustion-modes: Reactivity Controlled Compression Ignition and conventional diesel combustion. Meanwhile, also the carbon dioxide emissions limitations must be fulfilled. To achieve this goal, the benefits associated to powertrain electrification in terms of fuel economy, can be joined with the benefits of RCCI combustion. To do so, two different supervisory control strategies are compared: Adaptive Equivalent Minimization Control Strategy and Rule-Based Control strategy, while dynamic programming is used to size the electric grid of the powertrain to provide the best optimal solution in terms of fuel economy and emissions abatement. The analysis of the designed hybrid powertrain is carried out numerically with GT-Suite and Matlab-Simulink software. The results show a great potential of the parallel full-hybrid electric vehicle powertrain equipped with the dual-mode engine to reduce the engine-out emissions, also to increase fuel economy with respect to the homologation fuel consumption of the baseline vehicle. The optimal supervisory control strategy was found to be the emissions-oriented Adaptive Equivalent Minimization Control Strategy, which scores a simultaneous reduction of 12% in fuel consumption, 75% in engine-out nitrogen oxides emissions and 82% in engine-out soot, with respect to the baseline conventional diesel combustion engine vehicle.

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  • García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2020. "Energy management strategies comparison for a parallel full hybrid electric vehicle using Reactivity Controlled Compression Ignition combustion," Applied Energy, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:appene:v:272:y:2020:i:c:s0306261920307030
    DOI: 10.1016/j.apenergy.2020.115191
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    3. 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).
    4. Wei, Changyin & Chen, Yong & Li, Xiaoyu & Lin, Xiaozhe, 2022. "Integrating intelligent driving pattern recognition with adaptive energy management strategy for extender range electric logistics vehicle," Energy, Elsevier, vol. 247(C).
    5. Zhang, Hao & Liu, Shang & Lei, Nuo & Fan, Qinhao & Wang, Zhi, 2022. "Leveraging the benefits of ethanol-fueled advanced combustion and supervisory control optimization in hybrid biofuel-electric vehicles," Applied Energy, Elsevier, vol. 326(C).
    6. García, Antonio & Carlucci, Paolo & Monsalve-Serrano, Javier & Valletta, Andrea & Martínez-Boggio, Santiago, 2021. "Energy management optimization for a power-split hybrid in a dual-mode RCCI-CDC engine," Applied Energy, Elsevier, vol. 302(C).
    7. Wei, Changyin & Sun, Xiuxiu & Chen, Yong & Zang, Libin & Bai, Shujie, 2021. "Comparison of architecture and adaptive energy management strategy for plug-in hybrid electric logistics vehicle," Energy, Elsevier, vol. 230(C).
    8. 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).
    9. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2022. "Real-Time Energy Management Strategy Based on Driving Conditions Using a Feature Fusion Extreme Learning Machine," Energies, MDPI, vol. 15(12), pages 1-22, June.
    10. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    11. Cui, Wei & Cui, Naxin & Li, Tao & Cui, Zhongrui & Du, Yi & Zhang, Chenghui, 2022. "An efficient multi-objective hierarchical energy management strategy for plug-in hybrid electric vehicle in connected scenario," Energy, Elsevier, vol. 257(C).

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