IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i21p5311-d1506483.html
   My bibliography  Save this article

A Fuzzy Adaptive PID Coordination Control Strategy Based on Particle Swarm Optimization for Auxiliary Power Unit

Author

Listed:
  • Hongyan Qin

    (The School of Mechanical and Electrical Engineering, Sanjiang University, Nanjing 210012, China)

  • Lingfeng Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Shilong Wang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Weitao Ruan

    (College of Engineering, China Agricultural University, Beijing 100083, China)

  • Fachao Jiang

    (College of Engineering, China Agricultural University, Beijing 100083, China)

Abstract

Range extender hybrid vehicles have the advantages of better dynamics and longer driving range while reducing pollution and fuel consumption. This work focuses on the control strategy of an Auxiliary Power Unit (APU) operating in power generation mode for a range-extender mixer truck. When an operating point is switched, the engine speed and generator torque of the APU will switch accordingly. In order to ensure APU fast and stable adjustment to meet the power demand of the vehicle as well as operate at the lowest fuel consumption, a fuzzy adaptive PID coordination control strategy based on particle swarm optimization (PSO) is proposed to control the APU. The optimal operating curve of APU is calculated by coupling the engine and generator first. Then, the adaptive PID algorithm is used to control the speed and torque of the APU in a dual closed loop. The PSO is used to optimize the PID control parameter. Through hardware-in-the-loop tests under different working conditions, the control strategy is verified to be effective and real-time. The results show that the proposed control strategy can coordinate the operating of engine and generator and control the APU to track target power stably and quickly under minimum fuel consumption. Compared with traditional PID control strategy, the overshoot, regulation time and steady-state error are reduced by 55.1%, 11.1% and 77.3%, respectively.

Suggested Citation

  • Hongyan Qin & Lingfeng Wang & Shilong Wang & Weitao Ruan & Fachao Jiang, 2024. "A Fuzzy Adaptive PID Coordination Control Strategy Based on Particle Swarm Optimization for Auxiliary Power Unit," Energies, MDPI, vol. 17(21), pages 1-16, October.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:21:p:5311-:d:1506483
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/21/5311/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/17/21/5311/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Yalian & Pei, Huanxin & Hu, Xiaosong & Liu, Yonggang & Hou, Cong & Cao, Dongpu, 2019. "Fuel economy optimization of power split hybrid vehicles: A rapid dynamic programming approach," Energy, Elsevier, vol. 166(C), pages 929-938.
    2. Li, Junqiu & Wang, Yihe & Chen, Jianwen & Zhang, Xiaopeng, 2017. "Study on energy management strategy and dynamic modeling for auxiliary power units in range-extended electric vehicles," Applied Energy, Elsevier, vol. 194(C), pages 363-375.
    3. Chen, Bo-Chiuan & Wu, Yuh-Yih & Tsai, Hsien-Chi, 2014. "Design and analysis of power management strategy for range extended electric vehicle using dynamic programming," Applied Energy, Elsevier, vol. 113(C), pages 1764-1774.
    4. Huang, Ying & Wang, Shilong & Li, Ke & Fan, Zhuwei & Xie, Haiming & Jiang, Fachao, 2023. "Multi-parameter adaptive online energy management strategy for concrete truck mixers with a novel hybrid powertrain considering vehicle mass," Energy, Elsevier, vol. 277(C).
    Full references (including those not matched with items on IDEAS)

    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. Zhang, Yuxin & Yang, Yalian & Zou, Yunge & Liu, Changdong, 2024. "Design of optimal control strategy for range extended electric vehicles considering additional noise, vibration and harshness constraints," Energy, Elsevier, vol. 310(C).
    2. Xiao, B. & Ruan, J. & Yang, W. & Walker, P.D. & Zhang, N., 2021. "A review of pivotal energy management strategies for extended range electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Ye Yang & Youtong Zhang & Jingyi Tian & Si Zhang, 2018. "Research on a Plug-In Hybrid Electric Bus Energy Management Strategy Considering Drivability," Energies, MDPI, vol. 11(8), pages 1-22, August.
    4. Paweł Krawczyk & Artur Kopczyński & Jakub Lasocki, 2022. "Modeling and Simulation of Extended-Range Electric Vehicle with Control Strategy to Assess Fuel Consumption and CO 2 Emission for the Expected Driving Range," Energies, MDPI, vol. 15(12), pages 1-41, June.
    5. Liu, Hanwu & Lei, Yulong & Fu, Yao & Li, Xingzhong, 2022. "A novel hybrid-point-line energy management strategy based on multi-objective optimization for range-extended electric vehicle," Energy, Elsevier, vol. 247(C).
    6. Hou, Daizheng & Sun, Qun & Bao, Chunjiang & Cheng, Xingqun & Guo, Hongqiang & Zhao, Ying, 2019. "An all-in-one design method for plug-in hybrid electric buses considering uncertain factor of driving cycles," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    7. Gye-Seong Lee & Dong-Hyun Kim & Jong-Ho Han & Myeong-Hwan Hwang & Hyun-Rok Cha, 2019. "Optimal Operating Point Determination Method Design for Range-Extended Electric Vehicles Based on Real Driving Tests," Energies, MDPI, vol. 12(5), pages 1-17, March.
    8. Wang, Yaxin & Lou, Diming & Xu, Ning & Fang, Liang & Tan, Piqiang, 2021. "Energy management and emission control for range extended electric vehicles," Energy, Elsevier, vol. 236(C).
    9. Geng, Wenran & Lou, Diming & Wang, Chen & Zhang, Tong, 2020. "A cascaded energy management optimization method of multimode power-split hybrid electric vehicles," Energy, Elsevier, vol. 199(C).
    10. 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).
    11. Liu, Hanwu & Lei, Yulong & Sun, Wencai & Chang, Cheng & Jiang, Wei & Liu, Yuwei & Hu, Jianlong, 2024. "Research on approximate optimal energy management and multi-objective optimization of connected automated range-extended electric vehicle," Energy, Elsevier, vol. 306(C).
    12. 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.
    13. Xingyue Jiang & Jianjun Hu & Meixia Jia & Yong Zheng, 2018. "Parameter Matching and Instantaneous Power Allocation for the Hybrid Energy Storage System of Pure Electric Vehicles," Energies, MDPI, vol. 11(8), pages 1-18, July.
    14. Zhou, Quan & Zhang, Wei & Cash, Scott & Olatunbosun, Oluremi & Xu, Hongming & Lu, Guoxiang, 2017. "Intelligent sizing of a series hybrid electric power-train system based on Chaos-enhanced accelerated particle swarm optimization," Applied Energy, Elsevier, vol. 189(C), pages 588-601.
    15. Tian, Yang & Zhao, Yin & Wang, Zhong & Zhang, Yahui & Miao, Yusen & Zhang, Lipeng & Wen, Guilin & Zhang, Nong, 2024. "Non-dominated sorting artificial rabbit multi-objective sizing optimization for a conceptual powertrain of a 6 × 4 battery electric tractor truck," Energy, Elsevier, vol. 304(C).
    16. Sagit Valeev & Natalya Kondratyeva, 2022. "Life Test Optimization for Gas Turbine Engine Based on Life Cycle Information Support and Modeling," Energies, MDPI, vol. 15(19), pages 1-15, September.
    17. Wang, Yue & Zeng, Xiaohua & Song, Dafeng, 2020. "Hierarchical optimal intelligent energy management strategy for a power-split hybrid electric bus based on driving information," Energy, Elsevier, vol. 199(C).
    18. Hung, Yi-Hsuan & Wu, Chien-Hsun, 2015. "A combined optimal sizing and energy management approach for hybrid in-wheel motors of EVs," Applied Energy, Elsevier, vol. 139(C), pages 260-271.
    19. Han, Dandan & E, Jiaqiang & Deng, Yuanwang & Chen, Jingwei & Leng, Erwei & Liao, Gaoliang & Zhao, Xiaohuan & Feng, Changling & Zhang, Feng, 2021. "A review of studies using hydrocarbon adsorption material for reducing hydrocarbon emissions from cold start of gasoline engine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    20. Zhu, Jianyun & Chen, Li & Wang, Xuefeng & Yu, Long, 2020. "Bi-level optimal sizing and energy management of hybrid electric propulsion systems," Applied Energy, Elsevier, vol. 260(C).

    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:17:y:2024:i:21:p:5311-:d:1506483. 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.