IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i17p11017-d905988.html
   My bibliography  Save this article

Strategy Design and Performance Analysis of an Electromechanical Flywheel Hybrid Scheme for Electric Vehicles

Author

Listed:
  • Binbin Sun

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Tianqi Gu

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Mengxue Xie

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Pengwei Wang

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Song Gao

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Xi Zhang

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

Energy management strategies are one of the key factors affecting the working efficiency of electric vehicle energy power systems. At present, electric vehicles will develop real-time and efficient energy management strategies according to the topology of on-board energy power system to improve the driving performance of vehicles. In this paper, a new electromechanical flywheel hybrid system is studied. Firstly, the characteristics of the topological scheme of the electromechanical flywheel hybrid system are analyzed, and the working modes are designed. Secondly, in order to improve the efficiency of vehicles’ energy utilization and ensure the real-time performance of the management strategy, an energy management strategy based on fuzzy rules is designed with the flywheel’s state of energy (SOE) as the key reference parameter. Then, considering the directional stability in the braking process, the braking force distribution strategy between the front axle and the rear axle is designed. In order to improve the braking energy recovery efficiency, the secondary distribution strategy consisting of a mechanical braking force and regenerative braking force on the front and rear axles is designed. Finally, the bench test of a electromechanical flywheel hybrid system is carried out. Experiments show that compared with the original dual-motor four-wheel drive scheme, the electromechanical flywheel hybrid four-wheel drive system scheme developed in this paper can reduce the current variation range of lithium batteries by 43.16%, increase the average efficiency by 1.04%, and increase the braking energy recovery rate by 40.61% under the Japan urban cycle conditions. In addition, taking advantage of the energy and power regulation advantages of the electromechanical flywheel device, the power consumption of the lithium battery is reduced by 1.82% under cycling conditions.

Suggested Citation

  • Binbin Sun & Tianqi Gu & Mengxue Xie & Pengwei Wang & Song Gao & Xi Zhang, 2022. "Strategy Design and Performance Analysis of an Electromechanical Flywheel Hybrid Scheme for Electric Vehicles," Sustainability, MDPI, vol. 14(17), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:17:p:11017-:d:905988
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/17/11017/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/17/11017/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huang, Chung-Neng & Chen, Yui-Sung, 2017. "Design of magnetic flywheel control for performance improvement of fuel cells used in vehicles," Energy, Elsevier, vol. 118(C), pages 840-852.
    2. Anil K. Madhusudhanan & Xiaoxiang Na & David Cebon, 2021. "A Computationally Efficient Framework for Modelling Energy Consumption of ICE and Electric Vehicles," Energies, MDPI, vol. 14(7), pages 1-15, April.
    3. Yuan, Xinmei & Zhang, Chuanpu & Hong, Guokai & Huang, Xueqi & Li, Lili, 2017. "Method for evaluating the real-world driving energy consumptions of electric vehicles," Energy, Elsevier, vol. 141(C), pages 1955-1968.
    4. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    5. Ganesh, Akhil Hannegudda & Xu, Bin, 2022. "A review of reinforcement learning based energy management systems for electrified powertrains: Progress, challenge, and potential solution," Renewable and Sustainable Energy Reviews, Elsevier, vol. 154(C).
    6. Liu, Yonggang & Liu, Junjun & Zhang, Yuanjian & Wu, Yitao & Chen, Zheng & Ye, Ming, 2020. "Rule learning based energy management strategy of fuel cell hybrid vehicles considering multi-objective optimization," Energy, Elsevier, vol. 207(C).
    7. Cao, Jidi & Chen, Xin & Qiu, Rui & Hou, Shuhua, 2021. "Electric vehicle industry sustainable development with a stakeholder engagement system," Technology in Society, Elsevier, vol. 67(C).
    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. Michael Neidhardt & Jordi Mas-Peiro & Antonia Schneck & Josep O. Pou & Rafael Gonzalez-Olmos & Arno Kwade & Benedikt Schmuelling, 2022. "Automotive Electrification Challenges Shown by Real-World Driving Data and Lifecycle Assessment," Sustainability, MDPI, vol. 14(23), pages 1-19, November.
    2. Xiaoping Li & Junming Zhou & Wei Guan & Feng Jiang & Guangming Xie & Chunfeng Wang & Weiguang Zheng & Zhijie Fang, 2023. "Optimization of Brake Feedback Efficiency for Small Pure Electric Vehicles Based on Multiple Constraints," Energies, MDPI, vol. 16(18), pages 1-20, September.
    3. Hridoy Roy & Bimol Nath Roy & Md. Hasanuzzaman & Md. Shahinoor Islam & Ayman S. Abdel-Khalik & Mostaf S. Hamad & Shehab Ahmed, 2022. "Global Advancements and Current Challenges of Electric Vehicle Batteries and Their Prospects: A Comprehensive Review," Sustainability, MDPI, vol. 14(24), pages 1-30, December.
    4. Nikita V. Martyushev & Boris V. Malozyomov & Ilham H. Khalikov & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Vadim Sergeevich Tynchenko & Yadviga Aleksandrovna Tynchenko & Mengxu , 2023. "Review of Methods for Improving the Energy Efficiency of Electrified Ground Transport by Optimizing Battery Consumption," Energies, MDPI, vol. 16(2), pages 1-39, January.
    5. Boris V. Malozyomov & Nikita V. Martyushev & Viktor Alekseevich Kukartsev & Vladislav Viktorovich Kukartsev & Sergei Vasilievich Tynchenko & Roman V. Klyuev & Nikolay A. Zagorodnii & Yadviga Aleksandr, 2023. "Study of Supercapacitors Built in the Start-Up System of the Main Diesel Locomotive," Energies, MDPI, vol. 16(9), pages 1-24, May.

    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. Macias, A. & Kandidayeni, M. & Boulon, L. & Trovão, J.P., 2021. "Fuel cell-supercapacitor topologies benchmark for a three-wheel electric vehicle powertrain," Energy, Elsevier, vol. 224(C).
    2. Mokesioluwa Fanoro & Mladen Božanić & Saurabh Sinha, 2022. "A Review of the Impact of Battery Degradation on Energy Management Systems with a Special Emphasis on Electric Vehicles," Energies, MDPI, vol. 15(16), pages 1-29, August.
    3. İnci, Mustafa & Büyük, Mehmet & Demir, Mehmet Hakan & İlbey, Göktürk, 2021. "A review and research on fuel cell electric vehicles: Topologies, power electronic converters, energy management methods, technical challenges, marketing and future aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    4. Fu, Zhengtang & Dong, Peiwu & Ju, Yanbing & Gan, Zhenkun & Zhu, Min, 2022. "An intelligent green vehicle management system for urban food reliably delivery:A case study of Shanghai, China," Energy, Elsevier, vol. 257(C).
    5. Sofiane Bacha & Ramzi Saadi & Mohamed Yacine Ayad & Mohamed Sahraoui & Khaled Laadjal & Antonio J. Marques Cardoso, 2023. "Autonomous Electric-Vehicle Control Using Speed Planning Algorithm and Back-Stepping Approach," Energies, MDPI, vol. 16(5), pages 1-26, March.
    6. 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).
    7. Ku, Donggyun & Choi, Minje & Yoo, Nakyoung & Shin, Seungheon & Lee, Seungjae, 2021. "A new algorithm for eco-friendly path guidance focused on electric vehicles," Energy, Elsevier, vol. 233(C).
    8. Feiyu Hou & Fei Yao & Zheng Li, 2022. "A Torque-Compensated Fault-Tolerant Control Method for Electric Vehicle Traction Motor with Short-Circuit Fault," Sustainability, MDPI, vol. 14(21), pages 1-17, October.
    9. Matteo Acquarone & Claudio Maino & Daniela Misul & Ezio Spessa & Antonio Mastropietro & Luca Sorrentino & Enrico Busto, 2023. "Influence of the Reward Function on the Selection of Reinforcement Learning Agents for Hybrid Electric Vehicles Real-Time Control," Energies, MDPI, vol. 16(6), pages 1-22, March.
    10. Huang, Ruchen & He, Hongwen & Gao, Miaojue, 2023. "Training-efficient and cost-optimal energy management for fuel cell hybrid electric bus based on a novel distributed deep reinforcement learning framework," Applied Energy, Elsevier, vol. 346(C).
    11. Jie Hu & Wentong Cao & Feng Jiang & Lingling Hu & Qian Chen & Weiguang Zheng & Junming Zhou, 2023. "Study on Multi-Objective Optimization of Power System Parameters of Battery Electric Vehicles," Sustainability, MDPI, vol. 15(10), pages 1-23, May.
    12. M. M. Hasan & Shakhawat Hossain & M. Mofijur & Zobaidul Kabir & Irfan Anjum Badruddin & T. M. Yunus Khan & Esam Jassim, 2023. "Harnessing Solar Power: A Review of Photovoltaic Innovations, Solar Thermal Systems, and the Dawn of Energy Storage Solutions," Energies, MDPI, vol. 16(18), pages 1-30, September.
    13. Yossi Hadad & Baruch Keren & Dima Alberg, 2023. "An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements," Energies, MDPI, vol. 16(11), pages 1-18, May.
    14. Hicham El Hadraoui & Mourad Zegrari & Fatima-Ezzahra Hammouch & Nasr Guennouni & Oussama Laayati & Ahmed Chebak, 2022. "Design of a Customizable Test Bench of an Electric Vehicle Powertrain for Learning Purposes Using Model-Based System Engineering," Sustainability, MDPI, vol. 14(17), pages 1-22, September.
    15. Yanhua Liang & Hongjuan Lu, 2022. "Dynamic Evaluation and Regional Differences Analysis of the NEV Industry Development in China," Sustainability, MDPI, vol. 14(21), pages 1-23, October.
    16. Daniel Egan & Qilun Zhu & Robert Prucka, 2023. "A Review of Reinforcement Learning-Based Powertrain Controllers: Effects of Agent Selection for Mixed-Continuity Control and Reward Formulation," Energies, MDPI, vol. 16(8), pages 1-31, April.
    17. Bray, Garrett & Cebon, David, 2022. "Operational speed strategy opportunities for autonomous trucking on highways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 75-94.
    18. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
    19. Theo Lieven & Beatrice Hügler, 2021. "Did Electric Vehicle Sales Skyrocket Due to Increased Environmental Awareness While Total Vehicle Sales Declined during COVID-19?," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    20. Wu, Yingwen & Ji, Yangjian, 2023. "Identifying firm-specific technology opportunities from the perspective of competitors by using association rule mining," Journal of Informetrics, Elsevier, vol. 17(2).

    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:jsusta:v:14:y:2022:i:17:p:11017-:d:905988. 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.