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Technoeconomic decision support for second-life batteries

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  • Zhuang, Jihan
  • Bach, Amadeus
  • van Vlijmen, Bruis H.C.
  • Reichelstein, Stefan J.
  • Chueh, William
  • Onori, Simona
  • Benson, Sally M.

Abstract

The growth of electric vehicles (EVs) has raised concerns about the disposition of their batteries once they reach the end of their life. Currently, recycling is regarded as the potential solution for retired Li-ion batteries (LIBs). However, these LIBs can still be repurposed for other energy storage system (ESS) applications in their “second life” before recycling. Yet, there is no guidance for deciding whether to reuse or recycle them. Here, a technoeconomic decision support model is proposed to evaluate retired batteries from both technical and economic perspectives. Data-driven models are developed and combined with an equivalent circuit model (ECM) to build module-level aging models. Simulations show that limiting the State of Charge (SOC) operating range and charge current in second life applications can extend the lifetime of LIBs. Depending on when and how to use the battery in its second life, the simulated lifetime is between 1–6 years. From an economic perspective, the most profitable application is frequency regulation, which has a value of $/kWh. A comprehensive comparison of different end-of-life strategies is presented to demonstrate the most economically way to handle a retired battery.

Suggested Citation

  • Zhuang, Jihan & Bach, Amadeus & van Vlijmen, Bruis H.C. & Reichelstein, Stefan J. & Chueh, William & Onori, Simona & Benson, Sally M., 2025. "Technoeconomic decision support for second-life batteries," Applied Energy, Elsevier, vol. 390(C).
    • Handle: RePEc:eee:appene:v:390:y:2025:i:c:s0306261925005306
    DOI: 10.1016/j.apenergy.2025.125800
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    1. 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.
    2. Tang, Xiaopeng & Zou, Changfu & Yao, Ke & Lu, Jingyi & Xia, Yongxiao & Gao, Furong, 2019. "Aging trajectory prediction for lithium-ion batteries via model migration and Bayesian Monte Carlo method," Applied Energy, Elsevier, vol. 254(C).
    3. Penelope K. Jones & Ulrich Stimming & Alpha A. Lee, 2022. "Impedance-based forecasting of lithium-ion battery performance amid uneven usage," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    4. Peter M. Attia & Aditya Grover & Norman Jin & Kristen A. Severson & Todor M. Markov & Yang-Hung Liao & Michael H. Chen & Bryan Cheong & Nicholas Perkins & Zi Yang & Patrick K. Herring & Muratahan Ayko, 2020. "Closed-loop optimization of fast-charging protocols for batteries with machine learning," Nature, Nature, vol. 578(7795), pages 397-402, February.
    5. Mathews, Ian & Xu, Bolun & He, Wei & Barreto, Vanessa & Buonassisi, Tonio & Peters, Ian Marius, 2020. "Technoeconomic model of second-life batteries for utility-scale solar considering calendar and cycle aging," Applied Energy, Elsevier, vol. 269(C).
    6. Xia, Quan & Yang, Dezhen & Wang, Zili & Ren, Yi & Sun, Bo & Feng, Qiang & Qian, Cheng, 2020. "Multiphysical modeling for life analysis of lithium-ion battery pack in electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    7. Tadeusz Białoń & Roman Niestrój & Wojciech Skarka & Wojciech Korski, 2023. "HPPC Test Methodology Using LFP Battery Cell Identification Tests as an Example," Energies, MDPI, vol. 16(17), pages 1-21, August.
    8. Stephen Comello & Stefan Reichelstein, 2019. "The emergence of cost effective battery storage," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    9. Gavin Harper & Roberto Sommerville & Emma Kendrick & Laura Driscoll & Peter Slater & Rustam Stolkin & Allan Walton & Paul Christensen & Oliver Heidrich & Simon Lambert & Andrew Abbott & Karl Ryder & L, 2019. "Recycling lithium-ion batteries from electric vehicles," Nature, Nature, vol. 575(7781), pages 75-86, November.
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