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Methodology for Finding Maximum Performance and Improvement Possibility of Rule-Based Control for Parallel Type-2 Hybrid Electric Vehicles

Author

Listed:
  • Haeseong Jeoung

    (Machine Dynamics Laboratory, Department of Mechanical Engineering, Hanyang University, Gyeonggi-do, Ansan 15588, Korea)

  • Kiwook Lee

    (Machine Dynamics Laboratory, Department of Mechanical Engineering, Hanyang University, Gyeonggi-do, Ansan 15588, Korea)

  • Namwook Kim

    (Machine Dynamics Laboratory, Department of Mechanical Engineering, Hanyang University, Gyeonggi-do, Ansan 15588, Korea)

Abstract

Hybrid electric vehicles (HEVs) require supervisory controllers to distribute the propulsion power from sources like an engine and motors. Control concepts based on optimal control theories such as dynamic programming (DP) and Pontryagin’s minimum principle (PMP) have been studied to maximize fuel efficiencies. These concepts are, however, not practical for real-world applications because they guarantee optimality only if future driving information is given prior to the actual driving. Instead, heuristic rule-based control concepts are widely used in real-world applications. Those concepts are not only simple enough to be designed based on existing vehicle control concepts, but also allow developers to easily intervene in the control to enhance other vital aspects of real-world vehicle performances, such as safety and drivability. In this study, a rule-based control for parallel type-2 HEVs is developed based on representative control concepts of real-world HEVs, and optimal control parameters are determined by optimization processes. The performance of the optimized rule-based control is evaluated by comparing it with the optimal results obtained by PMP, and it shows that the rule-based concepts can achieve high fuel efficiencies, which are close, typically within 4%, to the maximum values obtained by PMP.

Suggested Citation

  • Haeseong Jeoung & Kiwook Lee & Namwook Kim, 2019. "Methodology for Finding Maximum Performance and Improvement Possibility of Rule-Based Control for Parallel Type-2 Hybrid Electric Vehicles," Energies, MDPI, vol. 12(10), pages 1-17, May.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:10:p:1924-:d:232741
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    References listed on IDEAS

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    Cited by:

    1. Woong Lee & Tacksu Kim & Jongryeol Jeong & Jaewoo Chung & Deokjin Kim & Beomho Lee & Namwook Kim, 2020. "Control Analysis of a Real-World P2 Hybrid Electric Vehicle Based on Test Data," Energies, MDPI, vol. 13(16), pages 1-15, August.
    2. Umberto Previti & Sebastian Brusca & Antonio Galvagno & Fabio Famoso, 2022. "Influence of Energy Management System Control Strategies on the Battery State of Health in Hybrid Electric Vehicles," Sustainability, MDPI, vol. 14(19), pages 1-20, September.

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    More about this item

    Keywords

    optimization; P2 HEV; rule-based control; large-scale simulation;
    All these keywords.

    JEL classification:

    • P2 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies

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