IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v261y2022ipbs0360544222020801.html
   My bibliography  Save this article

Extending battery lifetime for electric wheel loaders with electric-hydraulic hybrid powertrain

Author

Listed:
  • Zhang, Haoxiang
  • Wang, Feng
  • Xu, Bing
  • Fiebig, Wieslaw

Abstract

Electrification is the future trend for construction machinery due to its advantage of zero-carbon emission. The battery of electric construction machinery has an inevitable degradation phenomenon, which will increase the maintenance cost. In this paper a parallel electric-hydraulic hybrid powertrain is proposed to extend battery lifetime of an electric wheel loader by utilizing a hydraulic powertrain to provide and capture power during launch and braking. Different operation modes are analyzed and a rule-based energy management strategy is developed to determine the holding and switching conditions of each mode. Powertrains and battery aging models are introduced in detail and dynamic simulation is developed in Simulink. Results show that the parallel electric-hydraulic hybrid powertrain with the proposed strategy can reduce the battery full equivalent cycles, thereby achieving a 15.64% improvement in the battery lifetime compared with the pure electric powertrain. Moreover, influences of the strategy parameters on extending the battery lifetime are further discussed, which gives a parametric design guideline to the energy management strategy.

Suggested Citation

  • Zhang, Haoxiang & Wang, Feng & Xu, Bing & Fiebig, Wieslaw, 2022. "Extending battery lifetime for electric wheel loaders with electric-hydraulic hybrid powertrain," Energy, Elsevier, vol. 261(PB).
    • Handle: RePEc:eee:energy:v:261:y:2022:i:pb:s0360544222020801
    DOI: 10.1016/j.energy.2022.125190
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222020801
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.125190?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Xie, Shaobo & Hu, Xiaosong & Xin, Zongke & Brighton, James, 2019. "Pontryagin’s Minimum Principle based model predictive control of energy management for a plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 236(C), pages 893-905.
    2. Anselma, Pier Giuseppe & Kollmeyer, Phillip & Lempert, Jeremy & Zhao, Ziyu & Belingardi, Giovanni & Emadi, Ali, 2021. "Battery state-of-health sensitive energy management of hybrid electric vehicles: Lifetime prediction and ageing experimental validation," Applied Energy, Elsevier, vol. 285(C).
    3. Li, Shuangqi & He, Hongwen & Su, Chang & Zhao, Pengfei, 2020. "Data driven battery modeling and management method with aging phenomenon considered," Applied Energy, Elsevier, vol. 275(C).
    4. You, Gae-won & Park, Sangdo & Oh, Dukjin, 2016. "Real-time state-of-health estimation for electric vehicle batteries: A data-driven approach," Applied Energy, Elsevier, vol. 176(C), pages 92-103.
    5. Kristen A. Severson & Peter M. Attia & Norman Jin & Nicholas Perkins & Benben Jiang & Zi Yang & Michael H. Chen & Muratahan Aykol & Patrick K. Herring & Dimitrios Fraggedakis & Martin Z. Bazant & Step, 2019. "Data-driven prediction of battery cycle life before capacity degradation," Nature Energy, Nature, vol. 4(5), pages 383-391, May.
    6. Ramakrishnan, R. & Hiremath, Somashekhar S. & Singaperumal, M., 2014. "Design strategy for improving the energy efficiency in series hydraulic/electric synergy system," Energy, Elsevier, vol. 67(C), pages 422-434.
    7. Suri, Girish & Onori, Simona, 2016. "A control-oriented cycle-life model for hybrid electric vehicle lithium-ion batteries," Energy, Elsevier, vol. 96(C), pages 644-653.
    8. Lin, Tianliang & Lin, Yuanzheng & Ren, Haoling & Chen, Haibin & Chen, Qihuai & Li, Zhongshen, 2020. "Development and key technologies of pure electric construction machinery," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(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. Jichao Liu & Yanyan Liang & Zheng Chen & Wenpeng Chen, 2023. "Energy Management Strategies for Hybrid Loaders: Classification, Comparison and Prospect," Energies, MDPI, vol. 16(7), pages 1-23, March.
    2. Shabani, Masoume & Wallin, Fredrik & Dahlquist, Erik & Yan, Jinyue, 2023. "The impact of battery operating management strategies on life cycle cost assessment in real power market for a grid-connected residential battery application," Energy, Elsevier, vol. 270(C).
    3. Wang, Feng & Wu, Jiaming & Lin, Zichang & Zhang, Haoxiang & Xu, Bing, 2023. "A power-sharing electro-hydraulic actuator system to downsize electric motors for electric mobile machines," Energy, Elsevier, vol. 284(C).
    4. Zhang, Haoxiang & Wang, Feng & Lin, Zichang & Xu, Bing, 2023. "Optimization of speed trajectory for electric wheel loaders: Battery lifetime extension," Applied Energy, Elsevier, vol. 351(C).

    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. da Silva, Samuel Filgueira & Eckert, Jony Javorski & Corrêa, Fernanda Cristina & Silva, Fabrício Leonardo & Silva, Ludmila C.A. & Dedini, Franco Giuseppe, 2022. "Dual HESS electric vehicle powertrain design and fuzzy control based on multi-objective optimization to increase driving range and battery life cycle," Applied Energy, Elsevier, vol. 324(C).
    2. Rauf, Huzaifa & Khalid, Muhammad & Arshad, Naveed, 2022. "Machine learning in state of health and remaining useful life estimation: Theoretical and technological development in battery degradation modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    3. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    4. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
    5. S, Vignesh & Che, Hang Seng & Selvaraj, Jeyraj & Tey, Kok Soon & Lee, Jia Woon & Shareef, Hussain & Errouissi, Rachid, 2024. "State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges," Applied Energy, Elsevier, vol. 369(C).
    6. 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).
    7. Zhou, Yuekuan, 2024. "AI-driven battery ageing prediction with distributed renewable community and E-mobility energy sharing," Renewable Energy, Elsevier, vol. 225(C).
    8. Nagulapati, Vijay Mohan & Lee, Hyunjun & Jung, DaWoon & Brigljevic, Boris & Choi, Yunseok & Lim, Hankwon, 2021. "Capacity estimation of batteries: Influence of training dataset size and diversity on data driven prognostic models," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    9. Hassanzadeh, Mojtaba & Rahmani, Zahra, 2022. "A predictive controller for real-time energy management of plug-in hybrid electric vehicles," Energy, Elsevier, vol. 249(C).
    10. Zhang, Ying & Li, Yan-Fu, 2022. "Prognostics and health management of Lithium-ion battery using deep learning methods: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    11. Zhang, Wencan & Ouyang, Nan & Yin, Xiuxing & Li, Xingyao & Wu, Weixiong & Huang, Liansheng, 2022. "Data-driven early warning strategy for thermal runaway propagation in Lithium-ion battery modules with variable state of charge," Applied Energy, Elsevier, vol. 323(C).
    12. 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).
    13. 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).
    14. Kurucan, Mehmet & Özbaltan, Mete & Yetgin, Zeki & Alkaya, Alkan, 2024. "Applications of artificial neural network based battery management systems: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    15. Tang, Aihua & Xu, Yuchen & Hu, Yuanzhi & Tian, Jinpeng & Nie, Yuwei & Yan, Fuwu & Tan, Yong & Yu, Quanqing, 2024. "Battery state of health estimation under dynamic operations with physics-driven deep learning," Applied Energy, Elsevier, vol. 370(C).
    16. Cristobal Morales & Augusto Lismayes & Hector Chavez & Harold R. Chamorro & Lorenzo Reyes-Chamorro, 2021. "The Impact of Aging-Preventive Algorithms on BESS Sizing under AGC Performance Standards," Energies, MDPI, vol. 14(21), pages 1-13, November.
    17. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    18. Xie, Shaobo & Hu, Xiaosong & Qi, Shanwei & Tang, Xiaolin & Lang, Kun & Xin, Zongke & Brighton, James, 2019. "Model predictive energy management for plug-in hybrid electric vehicles considering optimal battery depth of discharge," Energy, Elsevier, vol. 173(C), pages 667-678.
    19. Wang, Hao & He, Hongwen & Bai, Yunfei & Yue, Hongwei, 2022. "Parameterized deep Q-network based energy management with balanced energy economy and battery life for hybrid electric vehicles," Applied Energy, Elsevier, vol. 320(C).
    20. Guo, Lingxiong & Zhang, Xudong & Zou, Yuan & Guo, Ningyuan & Li, Jianwei & Du, Guodong, 2021. "Cost-optimal energy management strategy for plug-in hybrid electric vehicles with variable horizon speed prediction and adaptive state-of-charge reference," Energy, Elsevier, vol. 232(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:eee:energy:v:261:y:2022:i:pb:s0360544222020801. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.