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Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon

Author

Listed:
  • Pierpaolo Polverino

    (Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, SA, Italy)

  • Ivan Arsie

    (Department of Engineering, University of Naples “Parthenope”, Centro Direzionale, 80143 Napoli, NA, Italy)

  • Cesare Pianese

    (Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, SA, Italy)

Abstract

Fuel consumption and emissions in parallel hybrid electric vehicles (HEVs) are directly linked to the way the load request to the wheels is managed between the internal combustion engine and the electric motor powered by the battery. A significant reduction in both consumption and emissions can be achieved by optimally controlling the power split on an entire driving mission (full horizon—FH). However, the entire driving path is often not predictable in real applications, hindering the fulfillment of the advantages gained through such an approach. An improvement can be achieved by exploiting more information available onboard, such as those derived from Advanced Driver Assistance Systems (ADAS) and vehicle connectivity (V2X). With this aim, the present work presents the design and verification, in a simulated environment, of an optimized controller for HEVs energy management, based on dynamic programming (DP) and receding horizon (RH) approaches. The control algorithm entails the partial knowledge of the driving mission, and its performance is assessed by evaluating fuel consumption related to a Worldwide harmonized Light vehicles Test Cycle (WLTC) under different control features (i.e., horizon length and update distance). The obtained results show a fuel consumption reduction comparable to that of the FH, with maximum drift from optimal consumption of less than 10%.

Suggested Citation

  • Pierpaolo Polverino & Ivan Arsie & Cesare Pianese, 2021. "Optimal Energy Management for Hybrid Electric Vehicles Based on Dynamic Programming and Receding Horizon," Energies, MDPI, vol. 14(12), pages 1-11, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:12:p:3502-:d:573989
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    References listed on IDEAS

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    Cited by:

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    2. Alberto Broatch & Pablo Olmeda & Benjamín Plá & Amin Dreif, 2022. "Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains," Energies, MDPI, vol. 16(1), pages 1-21, December.
    3. Fabrizio Donatantonio & Alessandro Ferrara & Pierpaolo Polverino & Ivan Arsie & Cesare Pianese, 2022. "Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions," Energies, MDPI, vol. 15(6), pages 1-22, March.
    4. Dong, Peng & Zhao, Junwei & Liu, Xuewu & Wu, Jian & Xu, Xiangyang & Liu, Yanfang & Wang, Shuhan & Guo, Wei, 2022. "Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages, challenges, and future trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    5. Grzegorz Karoń, 2022. "Safe and Effective Smart Urban Transportation—Energy Flow in Electric (EV) and Hybrid Electric Vehicles (HEV)," Energies, MDPI, vol. 15(18), pages 1-8, September.
    6. Vincenzo De Bellis & Marco Piras & Enrica Malfi, 2022. "Assessment of an Adaptive Efficient Thermal/Electric Skipping Control Strategy for the Management of a Parallel Plug-in Hybrid Electric Vehicle," Energies, MDPI, vol. 15(19), pages 1-20, September.
    7. Yangxun Liu & Weinan Liu & Huihong Liao & Hasier Ashan & Wenhua Zhou & Cangsu Xu, 2022. "An Experimental and a Kinetic Modelling Study of Ethanol/Acetone/Ethyl Acetate Mixtures," Energies, MDPI, vol. 15(9), pages 1-15, April.
    8. Kabir A. Mamun & F. R. Islam & R. Haque & Aneesh A. Chand & Kushal A. Prasad & Krishneel K. Goundar & Krishneel Prakash & Sidharth Maharaj, 2022. "Systematic Modeling and Analysis of On-Board Vehicle Integrated Novel Hybrid Renewable Energy System with Storage for Electric Vehicles," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    9. Andyn Omanovic & Norbert Zsiga & Patrik Soltic & Christopher Onder, 2021. "Optimal Degree of Hybridization for Spark-Ignited Engines with Optional Variable Valve Timings," Energies, MDPI, vol. 14(23), pages 1-21, December.

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