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Energy Optimization and Fuel Economy Investigation of a Series Hybrid Electric Vehicle Integrated with Diesel/RCCI Engines

Author

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  • Ali Solouk

    (Mechanical Engineering-Engineering Mechanics Department, Michigan Technological University, Houghton, MI 49931, USA)

  • Mahdi Shahbakhti

    (Mechanical Engineering-Engineering Mechanics Department, Michigan Technological University, Houghton, MI 49931, USA)

Abstract

Among different types of low temperature combustion (LTC) regimes, eactively controlled compression ignition (RCCI) has received a lot of attention as a promising advanced combustion engine technology with high indicated thermal efficiency and low nitrogen oxides ( NO x ) and particulate matter (PM) emissions. In this study, an RCCI engine for the purpose of fuel economy investigation is incorporated in series hybrid electric vehicle (SHEV) architecture, which allows the engine to run completely in the narrow RCCI mode for common driving cycles. Three different types of energy management control (EMC) strategies are designed and implemented to achieve the best fuel economy. The EMC strategies encompass rule-based control (RBC), offline, and online optimal controllers, including dynamic programing (DP) and model predictive control (MPC), respectively. The simulation results show a 13.1% to 14.2% fuel economy saving by using an RCCI engine over a modern spark ignition (SI) engine in SHEV for different driving cycles. This fuel economy saving is reduced to 3% in comparison with a modern compression ignition (CI) engine, while NO x emissions are significantly lower. Simulation results show that the RCCI engine offers more fuel economy improvement in more aggressive driving cycles (e.g., US06), compared to less aggressive driving cycles (e.g., UDDS). In addition, the MPC results show that sub-optimal fuel economy is achieved by predicting the vehicle speed profile for a time horizon of 70 s.

Suggested Citation

  • Ali Solouk & Mahdi Shahbakhti, 2016. "Energy Optimization and Fuel Economy Investigation of a Series Hybrid Electric Vehicle Integrated with Diesel/RCCI Engines," Energies, MDPI, vol. 9(12), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:12:p:1020-:d:84411
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    References listed on IDEAS

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    1. Hu, Xiaosong & Murgovski, Nikolce & Johannesson, Lars & Egardt, Bo, 2013. "Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes," Applied Energy, Elsevier, vol. 111(C), pages 1001-1009.
    2. Ximing Wang & Hongwen He & Fengchun Sun & Jieli Zhang, 2015. "Application Study on the Dynamic Programming Algorithm for Energy Management of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 8(4), pages 1-20, April.
    3. Jixiang Fan & Jiangyan Zhang & Tielong Shen, 2015. "Map-Based Power-Split Strategy Design with Predictive Performance Optimization for Parallel Hybrid Electric Vehicles," Energies, MDPI, vol. 8(9), pages 1-23, September.
    4. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    5. Shabbir, Wassif & Evangelou, Simos A., 2014. "Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency," Applied Energy, Elsevier, vol. 135(C), pages 512-522.
    6. Chaoying Xia & Cong Zhang, 2015. "Power Management Strategy of Hybrid Electric Vehicles Based on Quadratic Performance Index," Energies, MDPI, vol. 8(11), pages 1-16, November.
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    Cited by:

    1. Ioan Aschilean & Mihai Varlam & Mihai Culcer & Mariana Iliescu & Mircea Raceanu & Adrian Enache & Maria Simona Raboaca & Gabriel Rasoi & Constantin Filote, 2018. "Hybrid Electric Powertrain with Fuel Cells for a Series Vehicle," Energies, MDPI, vol. 11(5), pages 1-12, May.
    2. Abdullah U. Bajwa & Felix C. P. Leach & Martin H. Davy, 2023. "Prospects of Controlled Auto-Ignition Based Thermal Propulsion Units for Modern Gasoline Vehicles," Energies, MDPI, vol. 16(9), pages 1-45, May.
    3. Jinlong Hong & Liangchun Zhao & Yulong Lei & Bingzhao Gao, 2018. "Architecture Optimization of Hybrid Electric Vehicles with Future High-Efficiency Engine," Energies, MDPI, vol. 11(5), pages 1-23, May.
    4. Alessandro Benevieri & Lorenzo Carbone & Simone Cosso & Krishneel Kumar & Mario Marchesoni & Massimiliano Passalacqua & Luis Vaccaro, 2021. "Series Architecture on Hybrid Electric Vehicles: A Review," Energies, MDPI, vol. 14(22), pages 1-31, November.
    5. Dongwook Kim & Hongseok Kim & Anfeng Huang & Qiusen He & Hanyu Zhang & Seungyoung Ahn & Yuyu Zhu & Jun Fan, 2019. "Analysis and Introduction of Effective Permeability with Additional Air-Gaps on Wireless Power Transfer Coils for Electric Vehicle Based on SAE J2954 Recommended Practice," Energies, MDPI, vol. 12(24), pages 1-11, December.
    6. Chien-Hsun Wu & Yong-Xiang Xu, 2019. "The Optimal Control of Fuel Consumption for a Heavy-Duty Motorcycle with Three Power Sources Using Hardware-in-the-Loop Simulation," Energies, MDPI, vol. 13(1), pages 1-16, December.
    7. Armin Norouzi & Hamed Heidarifar & Mahdi Shahbakhti & Charles Robert Koch & Hoseinali Borhan, 2021. "Model Predictive Control of Internal Combustion Engines: A Review and Future Directions," Energies, MDPI, vol. 14(19), pages 1-40, October.
    8. Rezaei, A. & Burl, J.B. & Solouk, A. & Zhou, B. & Rezaei, M. & Shahbakhti, M., 2017. "Catch energy saving opportunity (CESO), an instantaneous optimal energy management strategy for series hybrid electric vehicles," Applied Energy, Elsevier, vol. 208(C), pages 655-665.
    9. 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).
    10. P. A. Harari & N. R. Banapurmath & V. S. Yaliwal & T. M. Yunus Khan & Irfan Anjum Badruddin & Sarfaraz Kamangar & Teuku Meurah Indra Mahlia, 2021. "Effect of Injection Timing and Injection Duration of Manifold Injected Fuels in Reactivity Controlled Compression Ignition Engine Operated with Renewable Fuels," Energies, MDPI, vol. 14(15), pages 1-19, July.
    11. Da Wang & Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Feng Xiao, 2018. "Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process," Energies, MDPI, vol. 11(5), pages 1-20, April.

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