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Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes

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  • Goh, Taedong
  • Park, Minjun
  • Seo, Minhwan
  • Kim, Jun Gu
  • Kim, Sang Woo

Abstract

Accurate diagnosis of battery degradation is important for safe and efficient battery management. Capacity is a reliable index to describe the state of health (SOH) in batteries. In this paper, a capacity estimation algorithm for Li-ion batteries with nickel, manganese, and cobalt (NMC) cathodes based on a second-order differential voltage is proposed. A reference voltage curve was obtained during the CC charging phase from a fresh battery beforehand, and the input voltage curve was measured and compared, under the same operating conditions, from an aged battery. The input voltage curve is aligned to the reference curve to minimize the error of the second-order differential voltage. The compensated charging time of the aligned curve has a linear relation with the battery capacity until capacity reduction reaches 23.5%. From the linear model, the capacity can be estimated easily. This method is verified for five packs aged with different discharge currents. In the aging cycle and the initial SOC variation test, the capacity estimation error is less than 2% until it reaches 76.5% capacity. The proposed method does not require a complete aging test (for the table) to relate the charging time and the capacity.

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  • Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2017. "Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes," Energy, Elsevier, vol. 135(C), pages 257-268.
    • Handle: RePEc:eee:energy:v:135:y:2017:i:c:p:257-268
    DOI: 10.1016/j.energy.2017.06.141
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    5. Zheng, Linfeng & Zhu, Jianguo & Wang, Guoxiu & Lu, Dylan Dah-Chuan & He, Tingting, 2018. "Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter," Energy, Elsevier, vol. 158(C), pages 1028-1037.
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    9. Sun, Tao & Wang, Shaoqing & Jiang, Sheng & Xu, Bowen & Han, Xuebing & Lai, Xin & Zheng, Yuejiu, 2022. "A cloud-edge collaborative strategy for capacity prognostic of lithium-ion batteries based on dynamic weight allocation and machine learning," Energy, Elsevier, vol. 239(PC).
    10. Qian, Cheng & Xu, Binghui & Chang, Liang & Sun, Bo & Feng, Qiang & Yang, Dezhen & Ren, Yi & Wang, Zili, 2021. "Convolutional neural network based capacity estimation using random segments of the charging curves for lithium-ion batteries," Energy, Elsevier, vol. 227(C).
    11. Goh, Taedong & Park, Minjun & Seo, Minhwan & Kim, Jun Gu & Kim, Sang Woo, 2018. "Successive-approximation algorithm for estimating capacity of Li-ion batteries," Energy, Elsevier, vol. 159(C), pages 61-73.
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    13. Pan, Haihong & Lü, Zhiqiang & Wang, Huimin & Wei, Haiyan & Chen, Lin, 2018. "Novel battery state-of-health online estimation method using multiple health indicators and an extreme learning machine," Energy, Elsevier, vol. 160(C), pages 466-477.
    14. Jiangong Zhu & Yixiu Wang & Yuan Huang & R. Bhushan Gopaluni & Yankai Cao & Michael Heere & Martin J. Mühlbauer & Liuda Mereacre & Haifeng Dai & Xinhua Liu & Anatoliy Senyshyn & Xuezhe Wei & Michael K, 2022. "Data-driven capacity estimation of commercial lithium-ion batteries from voltage relaxation," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    15. Zhang, Zhendong & Kong, Xiangdong & Zheng, Yuejiu & Zhou, Long & Lai, Xin, 2019. "Real-time diagnosis of micro-short circuit for Li-ion batteries utilizing low-pass filters," Energy, Elsevier, vol. 166(C), pages 1013-1024.
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    17. 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.
    18. Wang, Zengkai & Zeng, Shengkui & Guo, Jianbin & Qin, Taichun, 2019. "State of health estimation of lithium-ion batteries based on the constant voltage charging curve," Energy, Elsevier, vol. 167(C), pages 661-669.

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