IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i1p89-d125031.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/1/89/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/11/1/89/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Penghui Qiang & Peng Wu & Tao Pan & Huaiquan Zang, 2022. "Real-Time Energy Management Strategy Based on Driving Conditions Using a Feature Fusion Extreme Learning Machine," Energies, MDPI, vol. 15(12), pages 1-22, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shaobo Xie & Xiaosong Hu & Kun Lang & Shanwei Qi & Tong Liu, 2018. "Powering Mode-Integrated Energy Management Strategy for a Plug-In Hybrid Electric Truck with an Automatic Mechanical Transmission Based on Pontryagin’s Minimum Principle," Sustainability, MDPI, vol. 10(10), pages 1-23, October.
    2. Du, Jiuyu & Chen, Jingfu & Song, Ziyou & Gao, Mingming & Ouyang, Minggao, 2017. "Design method of a power management strategy for variable battery capacities range-extended electric vehicles to improve energy efficiency and cost-effectiveness," Energy, Elsevier, vol. 121(C), pages 32-42.
    3. Zhang, Shuo & Hu, Xiaosong & Xie, Shaobo & Song, Ziyou & Hu, Lin & Hou, Cong, 2019. "Adaptively coordinated optimization of battery aging and energy management in plug-in hybrid electric buses," Applied Energy, Elsevier, vol. 256(C).
    4. Yuping Zeng & Yang Cai & Changbao Chu & Guiyue Kou & Wei Gao, 2018. "Integrated Energy and Catalyst Thermal Management for Plug-In Hybrid Electric Vehicles," Energies, MDPI, vol. 11(7), pages 1-29, July.
    5. Hyunhwa Kim & Junbeom Wi & Jiho Yoo & Hanho Son & Chiman Park & Hyunsoo Kim, 2018. "A Study on the Fuel Economy Potential of Parallel and Power Split Type Hybrid Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-19, August.
    6. Jing Lian & Shuang Liu & Linhui Li & Xuanzuo Liu & Yafu Zhou & Fan Yang & Lushan Yuan, 2017. "A Mixed Logical Dynamical-Model Predictive Control (MLD-MPC) Energy Management Control Strategy for Plug-in Hybrid Electric Vehicles (PHEVs)," Energies, MDPI, vol. 10(1), pages 1-18, January.
    7. 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.
    8. Tian, He & Lu, Ziwang & Wang, Xu & Zhang, Xinlong & Huang, Yong & Tian, Guangyu, 2016. "A length ratio based neural network energy management strategy for online control of plug-in hybrid electric city bus," Applied Energy, Elsevier, vol. 177(C), pages 71-80.
    9. Zhang, Fengqi & Xiao, Lehua & Coskun, Serdar & Pang, Hui & Xie, Shaobo & Liu, Kailong & Cui, Yahui, 2023. "Comparative study of energy management in parallel hybrid electric vehicles considering battery ageing," Energy, Elsevier, vol. 264(C).
    10. Yuying Wang & Xiaohong Jiao & Zitao Sun & Ping Li, 2017. "Energy Management Strategy in Consideration of Battery Health for PHEV via Stochastic Control and Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 10(11), pages 1-21, November.
    11. Hongwei Liu & Chantong Wang & Xin Zhao & Chong Guo, 2018. "An Adaptive-Equivalent Consumption Minimum Strategy for an Extended-Range Electric Bus Based on Target Driving Cycle Generation," Energies, MDPI, vol. 11(7), pages 1-26, July.
    12. Liu, Teng & Tan, Wenhao & Tang, Xiaolin & Zhang, Jinwei & Xing, Yang & Cao, Dongpu, 2021. "Driving conditions-driven energy management strategies for hybrid electric vehicles: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    13. Bảo-Huy Nguyễn & João Pedro F. Trovão & Ronan German & Alain Bouscayrol, 2020. "Real-Time Energy Management of Parallel Hybrid Electric Vehicles Using Linear Quadratic Regulation," Energies, MDPI, vol. 13(21), pages 1-19, October.
    14. Guo, Hongqiang & Sun, Qun & Wang, Chong & Wang, Qinpu & Lu, Silong, 2018. "A systematic design and optimization method of transmission system and power management for a plug-in hybrid electric vehicle," Energy, Elsevier, vol. 148(C), pages 1006-1017.
    15. Da Wang & Chuanxue Song & Yulong Shao & Shixin Song & Silun Peng & Feng Xiao, 2018. "Optimal Control Strategy for Series Hybrid Electric Vehicles in the Warm-Up Process," Energies, MDPI, vol. 11(5), pages 1-20, April.
    16. Du, Guodong & Zou, Yuan & Zhang, Xudong & Liu, Teng & Wu, Jinlong & He, Dingbo, 2020. "Deep reinforcement learning based energy management for a hybrid electric vehicle," Energy, Elsevier, vol. 201(C).
    17. Anselma, Pier Giuseppe, 2022. "Computationally efficient evaluation of fuel and electrical energy economy of plug-in hybrid electric vehicles with smooth driving constraints," Applied Energy, Elsevier, vol. 307(C).
    18. Fengqi Zhang & Lihua Wang & Serdar Coskun & Hui Pang & Yahui Cui & Junqiang Xi, 2020. "Energy Management Strategies for Hybrid Electric Vehicles: Review, Classification, Comparison, and Outlook," Energies, MDPI, vol. 13(13), pages 1-35, June.
    19. Qi, Chunyang & Zhu, Yiwen & Song, Chuanxue & Yan, Guangfu & Xiao, Feng & Da wang, & Zhang, Xu & Cao, Jingwei & Song, Shixin, 2022. "Hierarchical reinforcement learning based energy management strategy for hybrid electric vehicle," Energy, Elsevier, vol. 238(PA).
    20. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Lang, Kun, 2018. "An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles," Energy, Elsevier, vol. 163(C), pages 837-848.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:1:p:89-:d:125031. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.