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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
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    References listed on IDEAS

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    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).
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