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Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning

Author

Listed:
  • Hanho Son

    (School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea)

  • Hyunhwa Kim

    (School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea)

  • Sungho Hwang

    (School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea)

  • Hyunsoo Kim

    (School of Mechanical Engineering, Sungkyunkwan University, Seobu-ro, Suwon-si 2066, Korea)

Abstract

This paper presents an advanced rule-based mode control strategy (ARBC) for a plug-in hybrid electric vehicle (PHEV) considering the driving cycle characteristics and present battery state of charge (SOC). Using dynamic programming (DP) results, the behavior of the optimal operating mode was investigated for city (UDDS×2, JC08 ×2) and highway (HWFET ×2, NEDC ×2) driving cycles. It was found that the operating mode selection varies according to the driving cycle characteristics and battery SOC. To consider these characteristics, a predictive mode control map was developed using the machine learning algorithm, and ARBC was proposed, which can be implemented in real-time environments. The performance of ARBC was evaluated by comparing it with rule-based mode control (RBC), which is a CD-CS mode control strategy. It was found that the equivalent fuel economy of ARBC was improved by 1.9–3.3% by selecting the proper operating mode from the viewpoint of system efficiency for the whole driving cycle, regardless of the battery SOC.

Suggested Citation

  • Hanho Son & Hyunhwa Kim & Sungho Hwang & Hyunsoo Kim, 2018. "Development of an Advanced Rule-Based Control Strategy for a PHEV Using Machine Learning," Energies, MDPI, vol. 11(1), pages 1-15, January.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:89-:d:125031
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    References listed on IDEAS

    as
    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. Onori, Simona & Tribioli, Laura, 2015. "Adaptive Pontryagin’s Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt," Applied Energy, Elsevier, vol. 147(C), pages 224-234.
    3. Hanho Son & Kyusik Park & Sungho Hwang & Hyunsoo Kim, 2017. "Design Methodology of a Power Split Type Plug-In Hybrid Electric Vehicle Considering Drivetrain Losses," Energies, MDPI, vol. 10(4), pages 1-18, March.
    4. Hanho Son & Hyunsoo Kim, 2016. "Development of Near Optimal Rule-Based Control for Plug-In Hybrid Electric Vehicles Taking into Account Drivetrain Component Losses," Energies, MDPI, vol. 9(6), pages 1-18, May.
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    Cited by:

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