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Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation

Author

Listed:
  • de Hoog, Joris
  • Timmermans, Jean-Marc
  • Ioan-Stroe, Daniel
  • Swierczynski, Maciej
  • Jaguemont, Joris
  • Goutam, Shovon
  • Omar, Noshin
  • Van Mierlo, Joeri
  • Van Den Bossche, Peter

Abstract

This paper presents the development of a semi-empirical combined lifetime model for a Nickel Manganese Cobalt Oxide (NMC) cathode and a graphite anode based cell, considered as one of the most promising candidates for the automotive industry. The development of this model was based on a thorough understanding of the degradation behavior of a 20-Ah NMC cell, based on the analysis of the results of an extensive test-matrix using 146 cells. This test-matrix was designed around four impact factors: temperature (25–45°C), Depth-of-Discharge (100–20% DoD), middle State-of-Charge (80–20% Mid-SoC) and current rates (C/3 to 2C). Gathering sufficient data for a mathematical model requires a huge time-investment, and the measurements gathered over the course of 2.5years offer a unique insight in the aging behavior of the NMC cells used in this study. Experimental results for cycling aging indicated that the capacity loss was strongly affected by the Depth-of-Discharge and temperature. For calendar aging, an initial increase in capacity was observed when stored at low State-of-Charges, due to electrochemical milling, while the deterioration of the capacity was affected most by high storage State-Of-Charges and storage temperatures. The developed combined lifetime model showed an error of less than 5% RMS compared to the measurement results after a Worldwide harmonized Light vehicles Test (WLTC) was applied for 18months.

Suggested Citation

  • de Hoog, Joris & Timmermans, Jean-Marc & Ioan-Stroe, Daniel & Swierczynski, Maciej & Jaguemont, Joris & Goutam, Shovon & Omar, Noshin & Van Mierlo, Joeri & Van Den Bossche, Peter, 2017. "Combined cycling and calendar capacity fade modeling of a Nickel-Manganese-Cobalt Oxide Cell with real-life profile validation," Applied Energy, Elsevier, vol. 200(C), pages 47-61.
    • Handle: RePEc:eee:appene:v:200:y:2017:i:c:p:47-61
    DOI: 10.1016/j.apenergy.2017.05.018
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    Cited by:

    1. Chen, Jingwei & E, Jiaqiang & Kang, Siyi & Zhao, Xiaohuan & Zhu, Hao & Deng, Yuanwang & Peng, Qingguo & Zhang, Zhiqing, 2019. "Modeling and characterization of the mass transfer and thermal mechanics of the power lithium manganate battery under charging process," Energy, Elsevier, vol. 187(C).
    2. Fabian Rücker & Ilka Schoeneberger & Till Wilmschen & Ahmed Chahbaz & Philipp Dechent & Felix Hildenbrand & Elias Barbers & Matthias Kuipers & Jan Figgener & Dirk Uwe Sauer, 2022. "A Comprehensive Electric Vehicle Model for Vehicle-to-Grid Strategy Development," Energies, MDPI, vol. 15(12), pages 1-31, June.
    3. Sahar Khaleghi & Yousef Firouz & Maitane Berecibar & Joeri Van Mierlo & Peter Van Den Bossche, 2020. "Ensemble Gradient Boosted Tree for SoH Estimation Based on Diagnostic Features," Energies, MDPI, vol. 13(5), pages 1-16, March.
    4. Joris De Hoog & Joris Jaguemont & Mohamed Abdel-Monem & Peter Van Den Bossche & Joeri Van Mierlo & Noshin Omar, 2018. "Combining an Electrothermal and Impedance Aging Model to Investigate Thermal Degradation Caused by Fast Charging," Energies, MDPI, vol. 11(4), pages 1-15, March.
    5. Li, Yi & Zou, Changfu & Berecibar, Maitane & Nanini-Maury, Elise & Chan, Jonathan C.-W. & van den Bossche, Peter & Van Mierlo, Joeri & Omar, Noshin, 2018. "Random forest regression for online capacity estimation of lithium-ion batteries," Applied Energy, Elsevier, vol. 232(C), pages 197-210.
    6. Khaleghi, Sahar & Karimi, Danial & Beheshti, S. Hamidreza & Hosen, Md. Sazzad & Behi, Hamidreza & Berecibar, Maitane & Van Mierlo, Joeri, 2021. "Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network," Applied Energy, Elsevier, vol. 282(PA).
    7. Haber, Marc & Azaïs, Philippe & Genies, Sylvie & Raccurt, Olivier, 2023. "Stress factor identification and Risk Probabilistic Number (RPN) analysis of Li-ion batteries based on worldwide electric vehicle usage," Applied Energy, Elsevier, vol. 343(C).
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    9. Fabian Steger & Jonathan Krogh & Lasantha Meegahapola & Hans-Georg Schweiger, 2022. "Calculating Available Charge and Energy of Lithium-Ion Cells Based on OCV and Internal Resistance," Energies, MDPI, vol. 15(21), pages 1-23, October.
    10. Juan Antonio López-Villanueva & Pablo Rodríguez-Iturriaga & Luis Parrilla & Salvador Rodríguez-Bolívar, 2023. "Application of Variable-Order Fractional Calculus to the Modeling of Calendar Aging in Lithium-Ion Batteries," Energies, MDPI, vol. 16(5), pages 1-18, March.
    11. 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.
    12. Omid Rahbari & Noshin Omar & Joeri Van Mierlo & Marc A. Rosen & Thierry Coosemans & Maitane Berecibar, 2019. "Electric Vehicle Battery Lifetime Extension through an Intelligent Double-Layer Control Scheme," Energies, MDPI, vol. 12(8), pages 1-24, April.
    13. Islam, Shirazul & Iqbal, Atif & Marzband, Mousa & Khan, Irfan & Al-Wahedi, Abdullah M.A.B., 2022. "State-of-the-art vehicle-to-everything mode of operation of electric vehicles and its future perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    14. Chu Wang & Yaohong Sun & Yinghui Gao & Ping Yan, 2023. "The Incremental Capacity Curves and Frequency Response Characteristic Evolution of Lithium Titanate Battery during Ultra-High-Rate Discharging Cycles," Energies, MDPI, vol. 16(8), pages 1-14, April.
    15. Yang, Yang & Yuan, Wei & Zhang, Xiaoqing & Ke, Yuzhi & Qiu, Zhiqiang & Luo, Jian & Tang, Yong & Wang, Chun & Yuan, Yuhang & Huang, Yao, 2020. "A review on structuralized current collectors for high-performance lithium-ion battery anodes," Applied Energy, Elsevier, vol. 276(C).
    16. Peng, Qiao & Li, Wei & Fowler, Michael & Chen, Tao & Jiang, Wei & Liu, Kailong, 2024. "Battery calendar degradation trajectory prediction: Data-driven implementation and knowledge inspiration," Energy, Elsevier, vol. 294(C).
    17. Jia Guo & Yaqi Li & Kjeld Pedersen & Daniel-Ioan Stroe, 2021. "Lithium-Ion Battery Operation, Degradation, and Aging Mechanism in Electric Vehicles: An Overview," Energies, MDPI, vol. 14(17), pages 1-22, August.
    18. Mishra, Partha Pratim & Latif, Aadil & Emmanuel, Michael & Shi, Ying & McKenna, Killian & Smith, Kandler & Nagarajan, Adarsh, 2020. "Analysis of degradation in residential battery energy storage systems for rate-based use-cases," Applied Energy, Elsevier, vol. 264(C).
    19. 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.
    20. Lysander De Sutter & Gert Berckmans & Mario Marinaro & Jelle Smekens & Yousef Firouz & Margret Wohlfahrt-Mehrens & Joeri Van Mierlo & Noshin Omar, 2018. "Comprehensive Aging Analysis of Volumetric Constrained Lithium-Ion Pouch Cells with High Concentration Silicon-Alloy Anodes," Energies, MDPI, vol. 11(11), pages 1-21, October.
    21. Luke Farrier & Richard Bucknall, 2020. "Investigating the Performance Capability of a Lithium-ion Battery System When Powering Future Pulsed Loads," Energies, MDPI, vol. 13(6), pages 1-15, March.
    22. Ma, Jian & Xu, Shu & Shang, Pengchao & ding, Yu & Qin, Weili & Cheng, Yujie & Lu, Chen & Su, Yuzhuan & Chong, Jin & Jin, Haizu & Lin, Yongshou, 2020. "Cycle life test optimization for different Li-ion power battery formulations using a hybrid remaining-useful-life prediction method," Applied Energy, Elsevier, vol. 262(C).
    23. Liu, Kailong & Ashwin, T.R. & Hu, Xiaosong & Lucu, Mattin & Widanage, W. Dhammika, 2020. "An evaluation study of different modelling techniques for calendar ageing prediction of lithium-ion batteries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    24. Christian Geisbauer & Katharina Wöhrl & Daniel Koch & Gudrun Wilhelm & Gerhard Schneider & Hans-Georg Schweiger, 2021. "Comparative Study on the Calendar Aging Behavior of Six Different Lithium-Ion Cell Chemistries in Terms of Parameter Variation," Energies, MDPI, vol. 14(11), pages 1-18, June.

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