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Multi-year field measurements of home storage systems and their use in capacity estimation

Author

Listed:
  • Jan Figgener

    (RWTH Aachen University
    RWTH Aachen University
    RWTH Aachen University
    JARA-Energy)

  • Jonas van Ouwerkerk

    (RWTH Aachen University
    RWTH Aachen University
    RWTH Aachen University
    JARA-Energy)

  • David Haberschusz

    (RWTH Aachen University
    RWTH Aachen University
    RWTH Aachen University
    JARA-Energy)

  • Jakob Bors

    (RWTH Aachen University
    RWTH Aachen University
    ACCURE Battery Intelligence GmbH)

  • Philipp Woerner

    (RWTH Aachen University
    RWTH Aachen University)

  • Marc Mennekes

    (RWTH Aachen University
    RWTH Aachen University
    ACCURE Battery Intelligence GmbH)

  • Felix Hildenbrand

    (RWTH Aachen University
    RWTH Aachen University
    RWTH Aachen University
    JARA-Energy)

  • Christopher Hecht

    (RWTH Aachen University
    RWTH Aachen University
    RWTH Aachen University
    JARA-Energy)

  • Kai-Philipp Kairies

    (RWTH Aachen University
    RWTH Aachen University
    ACCURE Battery Intelligence GmbH)

  • Oliver Wessels

    (RWTH Aachen University
    RWTH Aachen University)

  • Dirk Uwe Sauer

    (RWTH Aachen University
    RWTH Aachen University
    RWTH Aachen University
    JARA-Energy)

Abstract

Home storage systems play an important role in the integration of residential photovoltaic systems and have recently experienced strong market growth worldwide. However, standardized methods for quantifying capacity fade during field operation are lacking, and therefore the European batteries regulation demands the development of reliable and transparent state of health estimations. Here we present real-world data from 21 privately operated lithium-ion systems in Germany, based on up to 8 years of high-resolution field measurements. We develop a scalable capacity estimation method based on the operational data and validate it through regular field capacity tests. The results show that systems lose about two to three percentage points of usable capacity per year on average. Our contribution includes the publication of an impactful dataset comprising approximately 106 system years, 14 billion data points and 146 gigabytes, aiming to address the shortage of public datasets in this field.

Suggested Citation

  • Jan Figgener & Jonas van Ouwerkerk & David Haberschusz & Jakob Bors & Philipp Woerner & Marc Mennekes & Felix Hildenbrand & Christopher Hecht & Kai-Philipp Kairies & Oliver Wessels & Dirk Uwe Sauer, 2024. "Multi-year field measurements of home storage systems and their use in capacity estimation," Nature Energy, Nature, vol. 9(11), pages 1438-1447, November.
    • Handle: RePEc:nat:natene:v:9:y:2024:i:11:d:10.1038_s41560-024-01620-9
    DOI: 10.1038/s41560-024-01620-9
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    References listed on IDEAS

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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. Liu, Sijia & Winter, Michaela & Lewerenz, Meinert & Becker, Jan & Sauer, Dirk Uwe & Ma, Zeyu & Jiang, Jiuchun, 2019. "Analysis of cyclic aging performance of commercial Li4Ti5O12-based batteries at room temperature," Energy, Elsevier, vol. 173(C), pages 1041-1053.
    3. Matthieu Dubarry & Nahuel Costa & Dax Matthews, 2023. "Data-driven direct diagnosis of Li-ion batteries connected to photovoltaics," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    4. Matthieu Dubarry & David Beck, 2021. "Analysis of Synthetic Voltage vs. Capacity Datasets for Big Data Li-ion Diagnosis and Prognosis," Energies, MDPI, vol. 14(9), pages 1-24, April.
    5. 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.
    6. Yu, Quanqing & Xiong, Rui & Yang, Ruixin & Pecht, Michael G., 2019. "Online capacity estimation for lithium-ion batteries through joint estimation method," Applied Energy, Elsevier, vol. 255(C).
    7. Yunwei Zhang & Qiaochu Tang & Yao Zhang & Jiabin Wang & Ulrich Stimming & Alpha A. Lee, 2020. "Identifying degradation patterns of lithium ion batteries from impedance spectroscopy using machine learning," Nature Communications, Nature, vol. 11(1), pages 1-6, December.
    8. Kim, S.K. & Cho, K.H. & Kim, J.Y. & Byeon, G., 2019. "Field study on operational performance and economics of lithium-polymer and lead-acid battery systems for consumer load management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    9. Kevin Jacqué & Lucas Koltermann & Jan Figgener & Sebastian Zurmühlen & Dirk Uwe Sauer, 2022. "The Influence of Frequency Containment Reserve on the Operational Data and the State of Health of the Hybrid Stationary Large-Scale Storage System," Energies, MDPI, vol. 15(4), pages 1-18, February.
    10. Xu, Zhicheng & Wang, Jun & Lund, Peter D. & Zhang, Yaoming, 2021. "Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data," Energy, Elsevier, vol. 225(C).
    11. Arnaud Devie & George Baure & Matthieu Dubarry, 2018. "Intrinsic Variability in the Degradation of a Batch of Commercial 18650 Lithium-Ion Cells," Energies, MDPI, vol. 11(5), pages 1-14, April.
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