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Real-time control strategy to maximize hybrid electric vehicle powertrain efficiency

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  • Shabbir, Wassif
  • Evangelou, Simos A.

Abstract

The proposed supervisory control system (SCS) uses a control map to maximize the powertrain efficiency of a hybrid electric vehicle (HEV) in real-time. The paper presents the methodology and structure of the control, including a novel, comprehensive and unified expression for the overall powertrain efficiency that considers the engine-generator set and the battery in depth as well as the power electronics. A control map is then produced with instructions for the optimal power share between the engine branch and battery branch of the vehicle such that the powertrain efficiency is maximized. This map is computed off-line and can thereafter be operated in real-time at very low computational cost. A charge sustaining factor is also developed and introduced to ensure the SCS operates the vehicle within desired SOC bounds. This SCS is then tested and benchmarked against two conventional control strategies in a high-fidelity vehicle model, representing a series HEV. Extensive simulation results are presented for repeated cycles of a diverse range of standard driving cycles, showing significant improvements in fuel economy (up to 20%) and less aggressive use of the battery.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:135:y:2014:i:c:p:512-522
    DOI: 10.1016/j.apenergy.2014.08.083
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    Cited by:

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    2. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
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    5. 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.
    6. Yi, Chenyu & Epureanu, Bogdan I. & Hong, Sung-Kwon & Ge, Tony & Yang, Xiao Guang, 2016. "Modeling, control, and performance of a novel architecture of hybrid electric powertrain system," Applied Energy, Elsevier, vol. 178(C), pages 454-467.
    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. Piotr Wróblewski & Jerzy Kupiec & Wojciech Drożdż & Wojciech Lewicki & Jarosław Jaworski, 2021. "The Economic Aspect of Using Different Plug-In Hybrid Driving Techniques in Urban Conditions," Energies, MDPI, vol. 14(12), pages 1-17, June.
    9. Joshua Allwright & Akhlaqur Rahman & Marcus Coleman & Ambarish Kulkarni, 2022. "Heavy Multi-Articulated Vehicles with Electric and Hybrid Power Trains for Road Freight Activity: An Australian Context," Energies, MDPI, vol. 15(17), pages 1-19, August.
    10. Zheng, Yanchong & Shang, Yitong & Shao, Ziyun & Jian, Linni, 2018. "A novel real-time scheduling strategy with near-linear complexity for integrating large-scale electric vehicles into smart grid," Applied Energy, Elsevier, vol. 217(C), pages 1-13.
    11. Xiang, Changle & Ding, Feng & Wang, Weida & He, Wei, 2017. "Energy management of a dual-mode power-split hybrid electric vehicle based on velocity prediction and nonlinear model predictive control," Applied Energy, Elsevier, vol. 189(C), pages 640-653.
    12. Yuanjian Zhang & Liang Chu & Zicheng Fu & Nan Xu & Chong Guo & Yukuan Li & Zhouhuan Chen & Hanwen Sun & Qin Bai & Yang Ou, 2017. "An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World," Energies, MDPI, vol. 10(11), pages 1-23, November.
    13. Chaoying Xia & Zhiming DU & Cong Zhang, 2017. "A Single-Degree-of-Freedom Energy Optimization Strategy for Power-Split Hybrid Electric Vehicles," Energies, MDPI, vol. 10(7), pages 1-23, July.
    14. Cai, Y. & Yang, F. & Ouyang, MG., 2016. "Impact of control strategy on battery degradation for a plug-in hybrid electric city bus in China," Energy, Elsevier, vol. 116(P1), pages 1020-1030.
    15. Babu, Ajay & Ashok, S., 2015. "Improved parallel mild hybrids for urban roads," Applied Energy, Elsevier, vol. 144(C), pages 276-283.
    16. Miranda, Matheus H.R. & Silva, Fabrício L. & Lourenço, Maria A.M. & Eckert, Jony J. & Silva, Ludmila C.A., 2022. "Vehicle drivetrain and fuzzy controller optimization using a planar dynamics simulation based on a real-world driving cycle," Energy, Elsevier, vol. 257(C).
    17. Shabbir, Wassif & Evangelou, Simos A., 2019. "Threshold-changing control strategy for series hybrid electric vehicles," Applied Energy, Elsevier, vol. 235(C), pages 761-775.
    18. Tanaka, T. & Ito, S. & Muramatsu, M. & Yamada, T. & Kamiko, H. & Kakimoto, N. & Inui, Y., 2015. "Accurate and versatile simulation of transient voltage profile of lithium-ion secondary battery employing internal equivalent electric circuit," Applied Energy, Elsevier, vol. 143(C), pages 200-210.
    19. Xiaobo Sun & Weirong Liu & Mengfei Wen & Yue Wu & Heng Li & Jiahao Huang & Chao Hu & Zhiwu Huang, 2021. "A Real-Time Optimal Car-Following Power Management Strategy for Hybrid Electric Vehicles with ACC Systems," Energies, MDPI, vol. 14(12), pages 1-17, June.
    20. Apitzsch, Tilman & Klöffer, Christian & Jochem, Patrick & Doppelbauer, Martin & Fichtner, Wolf, 2016. "Metaheuristics for online drive train efficiency optimization in electric vehicles," Working Paper Series in Production and Energy 17, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
    21. Yang, Yalian & Hu, Xiaosong & Pei, Huanxin & Peng, Zhiyuan, 2016. "Comparison of power-split and parallel hybrid powertrain architectures with a single electric machine: Dynamic programming approach," Applied Energy, Elsevier, vol. 168(C), pages 683-690.
    22. Kong, Yan & Xu, Nan & Liu, Qiao & Sui, Yan & Yue, Fenglai, 2023. "A data-driven energy management method for parallel PHEVs based on action dependent heuristic dynamic programming (ADHDP) model," Energy, Elsevier, vol. 265(C).

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