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Novel Approaches for Energy Management Strategies of Hybrid Electric Vehicles and Comparison with Conventional Solutions

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  • Fabrizio Donatantonio

    (Department of Industrial Engineering, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
    Currently at Kineton s.r.l., via Gianturco 23, 84142 Napoli, Italy.)

  • Alessandro Ferrara

    (Department of Industrial Engineering, University of Salerno, via Giovanni Paolo II 132, 84084 Fisciano, Italy
    Currently at Institute of Mechanics and Mechatronics, Division of Process Control and Automation, TU Wien, Getreidemarkt 9/E325, 1060 Vienna, Austria.)

  • Pierpaolo Polverino

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

  • Ivan Arsie

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

  • Cesare Pianese

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

Abstract

Well-designed energy management strategies are essential for the good operation of Hybrid Electric Vehicles (HEVs) in terms of fuel economy and pollutant emissions reduction, regardless of the specific powertrain architecture. The goal of this paper is to propose two innovative supervisory control strategies for HEVs derived from different optimization algorithms and to assess HEVs’ fuel consumption reduction (compared to conventional vehicles). These approaches are derived from the literature and modified by the authors to present novel algorithms for the optimization problem. One is based on Dynamic Programming (DP), here referred to as the Forward Approach to Dynamic Programming (FADP) and introduces a different implementation of the DP to achieve computational and accuracy benefits. The other is based on the Equivalent Consumption Minimization Strategy (ECMS) approach, and it adapts to the latest driving conditions using information gathered in a finite-length backward-looking horizon. These techniques are used to achieve the optimal power share between the thermal engine and the battery of a parallel HEV. Their performances are compared and analysed in terms of achieved fuel economy and computational time with respect to conventional DP and Pontryagin’s Minimum Principle (PMP) approaches.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:6:p:1972-:d:766651
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    References listed on IDEAS

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    1. Zou Yuan & Liu Teng & Sun Fengchun & Huei Peng, 2013. "Comparative Study of Dynamic Programming and Pontryagin’s Minimum Principle on Energy Management for a Parallel Hybrid Electric Vehicle," Energies, MDPI, vol. 6(4), pages 1-14, April.
    2. Saiteja, Pemmareddy & Ashok, B., 2022. "Critical review on structural architecture, energy control strategies and development process towards optimal energy management in hybrid vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 157(C).
    3. Shi, Dehua & Liu, Sheng & Cai, Yingfeng & Wang, Shaohua & Li, Haoran & Chen, Long, 2021. "Pontryagin’s minimum principle based fuzzy adaptive energy management for hybrid electric vehicle using real-time traffic information," Applied Energy, Elsevier, vol. 286(C).
    4. Song, Ke & Wang, Xiaodi & Li, Feiqiang & Sorrentino, Marco & Zheng, Bailin, 2020. "Pontryagin’s minimum principle-based real-time energy management strategy for fuel cell hybrid electric vehicle considering both fuel economy and power source durability," Energy, Elsevier, vol. 205(C).
    5. 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.
    6. Enang, Wisdom & Bannister, Chris, 2017. "Modelling and control of hybrid electric vehicles (A comprehensive review)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 1210-1239.
    7. Maino, Claudio & Misul, Daniela & Musa, Alessia & Spessa, Ezio, 2021. "Optimal mesh discretization of the dynamic programming for hybrid electric vehicles," Applied Energy, Elsevier, vol. 292(C).
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    Cited by:

    1. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    2. Xueliang Li & Xinyu Kang & Xin Ba & Zengxiong Peng & Shujun Yang & Zhifu Zhao, 2022. "A Design Methodology for Dual-Mode Electro-Mechanical Transmission Scheme Based on Jointing Characteristics," Energies, MDPI, vol. 15(15), pages 1-15, July.

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