IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i17p6200-d1225749.html
   My bibliography  Save this article

Degradation Prediction and Cost Optimization of Second-Life Battery Used for Energy Arbitrage and Peak-Shaving in an Electric Grid

Author

Listed:
  • Rongheng Li

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Ali Hassan

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Nishad Gupte

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Wencong Su

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

  • Xuan Zhou

    (Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48128, USA)

Abstract

With the development of the electric vehicle industry, the number of batteries that are retired from vehicles is increasing rapidly, which raises critical environmental and waste issues. Second-life batteries recycled from automobiles have eighty percent of the capacity, which is a potential solution for the electricity grid application. To utilize the second-life batteries efficiently, an accurate estimation of their performance becomes a crucial portion of the optimization of cost-effectiveness. Nonetheless, few works focus on the modeling of the applications of second-life batteries. In this work, a general methodology is presented for the performance modeling and degradation prediction of second-life batteries applied in electric grid systems. The proposed method couples an electrochemical model of the battery performance, a state of health estimation method, and a revenue maximization algorithm for the application in the electric grid. The degradation of the battery is predicted under distinct charging and discharging rates. The results show that the degradation of the batteries can be slowed down, which is achieved by connecting numbers of batteries together in parallel to provide the same amount of required power. Many works aim for optimization of the operation of fresh Battery Energy Storage Systems (BESS). However, few works focus on the second-life battery applications. In this work, we present a trade-off between the revenue of the second-life battery and the service life while utilizing the battery for distinct operational strategies, i.e., arbitrage and peak shaving against Michigan’s DTE electricity utility’s Dynamic Peak Pricing (DPP) and Time of Use (TOU) tariffs. Results from case studies show that arbitrage against the TOU tariff in summer is the best choice due to its longer battery service life under the same power requirement. With the number of retired batteries set to increase over the next 10 years, this will give insight to the retired battery owners/procurers on how to increase the profitability, while making a circular economy of EV batteries more sustainable.

Suggested Citation

  • Rongheng Li & Ali Hassan & Nishad Gupte & Wencong Su & Xuan Zhou, 2023. "Degradation Prediction and Cost Optimization of Second-Life Battery Used for Energy Arbitrage and Peak-Shaving in an Electric Grid," Energies, MDPI, vol. 16(17), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:17:p:6200-:d:1225749
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/17/6200/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/17/6200/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Steckel, Tobiah & Kendall, Alissa & Ambrose, Hanjiro, 2021. "Applying levelized cost of storage methodology to utility-scale second-life lithium-ion battery energy storage systems," Applied Energy, Elsevier, vol. 300(C).
    3. Guoqing Luo & Yongzhi Zhang & Aihua Tang, 2023. "Capacity Degradation and Aging Mechanisms Evolution of Lithium-Ion Batteries under Different Operation Conditions," Energies, MDPI, vol. 16(10), pages 1-18, May.
    4. Lukáš Janota & Tomáš Králík & Jaroslav Knápek, 2020. "Second Life Batteries Used in Energy Storage for Frequency Containment Reserve Service," Energies, MDPI, vol. 13(23), pages 1-36, December.
    5. Christos S. Ioakimidis & Alberto Murillo-Marrodán & Ali Bagheri & Dimitrios Thomas & Konstantinos N. Genikomsakis, 2019. "Life Cycle Assessment of a Lithium Iron Phosphate (LFP) Electric Vehicle Battery in Second Life Application Scenarios," Sustainability, MDPI, vol. 11(9), pages 1-14, May.
    6. Michael Schimpe & Christian Piesch & Holger C. Hesse & Julian Paß & Stefan Ritter & Andreas Jossen, 2018. "Power Flow Distribution Strategy for Improved Power Electronics Energy Efficiency in Battery Storage Systems: Development and Implementation in a Utility-Scale System," Energies, MDPI, vol. 11(3), pages 1-17, March.
    7. Wang, Lei & Wang, Xiang & Yang, Wenxian, 2020. "Optimal design of electric vehicle battery recycling network – From the perspective of electric vehicle manufacturers," Applied Energy, Elsevier, vol. 275(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. Gu, Xubo & Bai, Hanyu & Cui, Xiaofan & Zhu, Juner & Zhuang, Weichao & Li, Zhaojian & Hu, Xiaosong & Song, Ziyou, 2024. "Challenges and opportunities for second-life batteries: Key technologies and economy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    2. Yash Kotak & Carlos Marchante Fernández & Lluc Canals Casals & Bhavya Satishbhai Kotak & Daniel Koch & Christian Geisbauer & Lluís Trilla & Alberto Gómez-Núñez & Hans-Georg Schweiger, 2021. "End of Electric Vehicle Batteries: Reuse vs. Recycle," Energies, MDPI, vol. 14(8), pages 1-15, April.
    3. Shi, Haotian & Wang, Shunli & Huang, Qi & Fernandez, Carlos & Liang, Jianhong & Zhang, Mengyun & Qi, Chuangshi & Wang, Liping, 2024. "Improved electric-thermal-aging multi-physics domain coupling modeling and identification decoupling of complex kinetic processes based on timescale quantification in lithium-ion batteries," Applied Energy, Elsevier, vol. 353(PB).
    4. Zhang, Chenxi & Yang, Yi & Wang, Yunqi & Qiu, Jing & Zhao, Junhua, 2024. "Auction-based peer-to-peer energy trading considering echelon utilization of retired electric vehicle second-life batteries," Applied Energy, Elsevier, vol. 358(C).
    5. Naseri, F. & Gil, S. & Barbu, C. & Cetkin, E. & Yarimca, G. & Jensen, A.C. & Larsen, P.G. & Gomes, C., 2023. "Digital twin of electric vehicle battery systems: Comprehensive review of the use cases, requirements, and platforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    6. Takyi-Aninakwa, Paul & Wang, Shunli & Zhang, Hongying & Yang, Xiao & Fernandez, Carlos, 2023. "A hybrid probabilistic correction model for the state of charge estimation of lithium-ion batteries considering dynamic currents and temperatures," Energy, Elsevier, vol. 273(C).
    7. Roman Gozdur & Tomasz Przerywacz & Dariusz Bogdański, 2021. "Low Power Modular Battery Management System with a Wireless Communication Interface," Energies, MDPI, vol. 14(19), pages 1-20, October.
    8. Ester Vasta & Tommaso Scimone & Giovanni Nobile & Otto Eberhardt & Daniele Dugo & Massimiliano Maurizio De Benedetti & Luigi Lanuzza & Giuseppe Scarcella & Luca Patanè & Paolo Arena & Mario Cacciato, 2023. "Models for Battery Health Assessment: A Comparative Evaluation," Energies, MDPI, vol. 16(2), pages 1-34, January.
    9. Okay, Kamil & Eray, Sermet & Eray, Aynur, 2022. "Development of prototype battery management system for PV system," Renewable Energy, Elsevier, vol. 181(C), pages 1294-1304.
    10. Pan, Rui & Liu, Tongshen & Huang, Wei & Wang, Yuxin & Yang, Duo & Chen, Jie, 2023. "State of health estimation for lithium-ion batteries based on two-stage features extraction and gradient boosting decision tree," Energy, Elsevier, vol. 285(C).
    11. Liu, Chang-Yi & Wang, Hui & Tang, Juan & Chang, Ching-Ter & Liu, Zhi, 2021. "Optimal recovery model in a used batteries closed-loop supply chain considering uncertain residual capacity," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 156(C).
    12. Tang, Xiaopeng & Liu, Kailong & Lu, Jingyi & Liu, Boyang & Wang, Xin & Gao, Furong, 2020. "Battery incremental capacity curve extraction by a two-dimensional Luenberger–Gaussian-moving-average filter," Applied Energy, Elsevier, vol. 280(C).
    13. Marmiroli, Benedetta & Venditti, Mattia & Dotelli, Giovanni & Spessa, Ezio, 2020. "The transport of goods in the urban environment: A comparative life cycle assessment of electric, compressed natural gas and diesel light-duty vehicles," Applied Energy, Elsevier, vol. 260(C).
    14. Qi, Kaijian & Zhang, Weigang & Zhou, Wei & Cheng, Jifu, 2022. "Integrated battery power capability prediction and driving torque regulation for electric vehicles: A reduced order MPC approach," Applied Energy, Elsevier, vol. 317(C).
    15. Jin Li & Feng Wang & Yu He, 2020. "Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions," Sustainability, MDPI, vol. 12(24), pages 1-20, December.
    16. Liu, Xinghua & Li, Siqi & Tian, Jiaqiang & Wei, Zhongbao & Wang, Peng, 2023. "Health estimation of lithium-ion batteries with voltage reconstruction and fusion model," Energy, Elsevier, vol. 282(C).
    17. Jessica Kersey & Natalie D. Popovich & Amol A. Phadke, 2022. "Rapid battery cost declines accelerate the prospects of all-electric interregional container shipping," Nature Energy, Nature, vol. 7(7), pages 664-674, July.
    18. Fu, Shiyi & Tao, Shengyu & Fan, Hongtao & He, Kun & Liu, Xutao & Tao, Yulin & Zuo, Junxiong & Zhang, Xuan & Wang, Yu & Sun, Yaojie, 2024. "Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method," Applied Energy, Elsevier, vol. 353(PA).
    19. Xingxing Wang & Peilin Ye & Shengren Liu & Yu Zhu & Yelin Deng & Yinnan Yuan & Hongjun Ni, 2023. "Research Progress of Battery Life Prediction Methods Based on Physical Model," Energies, MDPI, vol. 16(9), pages 1-20, April.
    20. Lai, Xin & Yao, Yi & Tang, Xiaopeng & Zheng, Yuejiu & Zhou, Yuanqiang & Sun, Yuedong & Gao, Furong, 2023. "Voltage profile reconstruction and state of health estimation for lithium-ion batteries under dynamic working conditions," Energy, Elsevier, vol. 282(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:16:y:2023:i:17:p:6200-:d:1225749. 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.