IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i17p5300-d622495.html
   My bibliography  Save this article

Energy Management Strategy for an Electromechanical-Hydraulic Coupled Power Electric Vehicle Considering the Optimal Speed Threshold

Author

Listed:
  • Zewen Meng

    (College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 260071, China
    Power Integration and Energy Storage Systems Engineering Technology Center (Qingdao), Qingdao 260071, China)

  • Tiezhu Zhang

    (College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 260071, China
    Power Integration and Energy Storage Systems Engineering Technology Center (Qingdao), Qingdao 260071, China)

  • Hongxin Zhang

    (College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 260071, China
    Power Integration and Energy Storage Systems Engineering Technology Center (Qingdao), Qingdao 260071, China)

  • Qinghai Zhao

    (College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 260071, China
    Power Integration and Energy Storage Systems Engineering Technology Center (Qingdao), Qingdao 260071, China)

  • Jian Yang

    (College of Mechanical and Electrical Engineering, Qingdao University, Qingdao 260071, China
    Power Integration and Energy Storage Systems Engineering Technology Center (Qingdao), Qingdao 260071, China)

Abstract

Considering the problems of the low energy recovery efficiency and the short driving range of pure electric vehicles, a new electromechanical–hydraulic coupled power electric vehicle is proposed. First, we develop an electromechanical–hydraulic coupled power electric vehicle model and design an energy management strategy to match it. On this basis, an optimization strategy is proposed with the goal of improving the braking energy recovery efficiency and avoiding the impact of high-speed braking energy recovery on the hydraulic system. The energy recovery mode conversion is optimized for different vehicle speeds when braking. Finally, the proposed optimization strategy is verified by joint simulation. The results show that when the vehicle speed is higher than 10 m/s for energy recovery mode switching, the total recovery efficiency of the whole vehicle increases to 97.273% and the SOC of the power battery increases by 0.14%. This provides strong support for improving the driving range of electromechanical–hydraulic coupled power electric vehicles.

Suggested Citation

  • Zewen Meng & Tiezhu Zhang & Hongxin Zhang & Qinghai Zhao & Jian Yang, 2021. "Energy Management Strategy for an Electromechanical-Hydraulic Coupled Power Electric Vehicle Considering the Optimal Speed Threshold," Energies, MDPI, vol. 14(17), pages 1-12, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5300-:d:622495
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/17/5300/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/17/5300/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Wei & Li, Yan & Shi, Man & Song, Yuling, 2021. "Optimization and control of battery-flywheel compound energy storage system during an electric vehicle braking," Energy, Elsevier, vol. 226(C).
    2. Zhang, Pei & Yan, Fuwu & Du, Changqing, 2015. "A comprehensive analysis of energy management strategies for hybrid electric vehicles based on bibliometrics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 88-104.
    3. Jian Yang & Tiezhu Zhang & Hongxin Zhang & Jichao Hong & Zewen Meng, 2020. "Research on the Starting Acceleration Characteristics of a New Mechanical–Electric–Hydraulic Power Coupling Electric Vehicle," Energies, MDPI, vol. 13(23), pages 1-20, November.
    4. Yafei Xin & Tiezhu Zhang & Hongxin Zhang & Qinghai Zhao & Jian Zheng & Congcong Wang, 2019. "Fuzzy Logic Optimization of Composite Brake Control Strategy for Load-Isolated Electric Bus," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-14, October.
    5. Torres, J.L. & Gonzalez, R. & Gimenez, A. & Lopez, J., 2014. "Energy management strategy for plug-in hybrid electric vehicles. A comparative study," Applied Energy, Elsevier, vol. 113(C), pages 816-824.
    6. Sarvaiya, Shradhdha & Ganesh, Sachin & Xu, Bin, 2021. "Comparative analysis of hybrid vehicle energy management strategies with optimization of fuel economy and battery life," Energy, Elsevier, vol. 228(C).
    7. Hong, Jichao & Wang, Zhenpo & Zhang, Tiezhu & Yin, Huaixian & Zhang, Hongxin & Huo, Wei & Zhang, Yi & Li, Yuanyuan, 2019. "Research on integration simulation and balance control of a novel load isolated pure electric driving system," Energy, Elsevier, vol. 189(C).
    8. Nikoobakht, Ahmad & Aghaei, Jamshid & Khatami, Roohallah & Mahboubi-Moghaddam, Esmaeel & Parvania, Masood, 2019. "Stochastic flexible transmission operation for coordinated integration of plug-in electric vehicles and renewable energy sources," Applied Energy, Elsevier, vol. 238(C), pages 225-238.
    9. Qiu, Chengqun & Wang, Guolin & Meng, Mingyu & Shen, Yujie, 2018. "A novel control strategy of regenerative braking system for electric vehicles under safety critical driving situations," Energy, Elsevier, vol. 149(C), pages 329-340.
    10. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    11. Hong, Jichao & Wang, Zhenpo & Yao, Yongtao, 2019. "Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    12. Wu, Wei & Hu, Jibin & Yuan, Shihua & Di, Chongfeng, 2016. "A hydraulic hybrid propulsion method for automobiles with self-adaptive system," Energy, Elsevier, vol. 114(C), pages 683-692.
    13. Jarosław Mamala & Michał Śmieja & Krzysztof Prażnowski, 2021. "Analysis of the Total Unit Energy Consumption of a Car with a Hybrid Drive System in Real Operating Conditions," Energies, MDPI, vol. 14(13), pages 1-16, July.
    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. Yang, Jian & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin & Zhao, Qinghai & Meng, Zewen, 2021. "Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle," Energy, Elsevier, vol. 233(C).
    2. Yang, Jian & Liu, Bo & Zhang, Tiezhu & Hong, Jichao & Zhang, Hongxin, 2023. "Multi-parameter controlled mechatronics-electro-hydraulic power coupling electric vehicle based on active energy regulation," Energy, Elsevier, vol. 263(PC).
    3. Jichao Hong & Fengwei Liang & Xun Gong & Xiaoming Xu & Quanqing Yu, 2022. "Accurate State of Charge Estimation for Real-World Battery Systems Using a Novel Grid Search and Cross Validated Optimised LSTM Neural Network," Energies, MDPI, vol. 15(24), pages 1-14, December.
    4. Li, Lin & Zhang, Tiezhu & Lu, Liqun & Zhang, Hongxin & Yang, Jian & Zhang, Zhen, 2023. "An energy active regulation management strategy based on driving mode recognition for electro-hydraulic hybrid vehicles," Energy, Elsevier, vol. 285(C).
    5. 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).
    6. Kaizhi Liang & Zhaosheng Zhang & Peng Liu & Zhenpo Wang & Shangfeng Jiang, 2019. "Data-Driven Ohmic Resistance Estimation of Battery Packs for Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-17, December.
    7. Yun Sun & Hongxin Zhang & Zhen Liang & Jian Yang, 2021. "Design Optimization of Electrodynamic Structure of Permanent Magnet Piston Mechanical Electric Engine," Energies, MDPI, vol. 14(19), pages 1-20, October.
    8. M. Sabri, M.F. & Danapalasingam, K.A. & Rahmat, M.F., 2016. "A review on hybrid electric vehicles architecture and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1433-1442.
    9. Zhao, Jingyuan & Feng, Xuning & Wang, Junbin & Lian, Yubo & Ouyang, Minggao & Burke, Andrew F., 2023. "Battery fault diagnosis and failure prognosis for electric vehicles using spatio-temporal transformer networks," Applied Energy, Elsevier, vol. 352(C).
    10. Peng, Jiankun & He, Hongwen & Xiong, Rui, 2017. "Rule based energy management strategy for a series–parallel plug-in hybrid electric bus optimized by dynamic programming," Applied Energy, Elsevier, vol. 185(P2), pages 1633-1643.
    11. Jian Yang & Tiezhu Zhang & Hongxin Zhang & Jichao Hong & Zewen Meng, 2020. "Research on the Starting Acceleration Characteristics of a New Mechanical–Electric–Hydraulic Power Coupling Electric Vehicle," Energies, MDPI, vol. 13(23), pages 1-20, November.
    12. Cong Geng & Dawen Ning & Linfu Guo & Qicheng Xue & Shujian Mei, 2021. "Simulation Research on Regenerative Braking Control Strategy of Hybrid Electric Vehicle," Energies, MDPI, vol. 14(8), pages 1-19, April.
    13. Hong, Jichao & Wang, Zhenpo & Qu, Changhui & Zhou, Yangjie & Shan, Tongxin & Zhang, Jinghan & Hou, Yankai, 2022. "Investigation on overcharge-caused thermal runaway of lithium-ion batteries in real-world electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    14. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
    15. Bizon, Nicu, 2019. "Real-time optimization strategies of Fuel Cell Hybrid Power Systems based on Load-following control: A new strategy, and a comparative study of topologies and fuel economy obtained," Applied Energy, Elsevier, vol. 241(C), pages 444-460.
    16. Cui, Binghan & Wang, Han & Li, Renlong & Xiang, Lizhi & Zhao, Huaian & Xiao, Rang & Li, Sai & Liu, Zheng & Yin, Geping & Cheng, Xinqun & Ma, Yulin & Huo, Hua & Zuo, Pengjian & Lu, Taolin & Xie, Jingyi, 2024. "Ultra-early prediction of lithium-ion battery performance using mechanism and data-driven fusion model," Applied Energy, Elsevier, vol. 353(PA).
    17. Ma, Zhikai & Huo, Qian & Wang, Wei & Zhang, Tao, 2023. "Voltage-temperature aware thermal runaway alarming framework for electric vehicles via deep learning with attention mechanism in time-frequency domain," Energy, Elsevier, vol. 278(C).
    18. 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.
    19. Cordiner, Stefano & Galeotti, Matteo & Mulone, Vincenzo & Nobile, Matteo & Rocco, Vittorio, 2016. "Trip-based SOC management for a plugin hybrid electric vehicle," Applied Energy, Elsevier, vol. 164(C), pages 891-905.
    20. Zhijie Duan & Luo Zhang & Lili Feng & Shuguang Yu & Zengyou Jiang & Xiaoming Xu & Jichao Hong, 2021. "Research on Economic and Operating Characteristics of Hydrogen Fuel Cell Cars Based on Real Vehicle Tests," Energies, MDPI, vol. 14(23), pages 1-19, November.

    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:14:y:2021:i:17:p:5300-:d:622495. 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.