Performance Degradation Evaluation of a Lithium-Ion Battery from Multiple SoC Measurements
(1) Institut Teknologi Kalimantan
(2) Institut Teknologi Kalimantan
(3) Institut Teknologi Kalimantan
(4) Institut Teknologi Kalimantan
Abstract
Keywords
Full Text:
PDFReferences
A. Gismero, E. Schaltz, and D. I. Stroe, “Recursive state of charge and state of health estimation method for lithium-ion batteries based on coulomb counting and open circuit voltage,” Energies, vol. 13, no. 7, p. 1811, 2020, doi: 10.3390/en13071811.
S. Zhang, X. Guo, X. Dou, and X. Zhang, “A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis,” J. Power Sources, vol. 479, p. 228740, Dec. 2020, doi: 10.1016/J.JPOWSOUR.2020.228740.
S. M. Qaisar, “Event-Driven Coulomb Counting for Effective Online Approximation of Li-Ion Battery State of Charge,” Energies 2020, Vol. 13, Page 5600, vol. 13, no. 21, p. 5600, Oct. 2020, doi: 10.3390/EN13215600.
N. Shateri, Z. Shi, D. J. Auger, and A. Fotouhi, “Lithium-Sulfur Cell State of Charge Estimation Using a Classification Technique,” IEEE Trans. Veh. Technol., vol. 70, no. 1, pp. 212–224, 2021, doi: 10.1109/TVT.2020.3045213.
X. Tang, F. Gao, C. Zou, K. Yao, W. Hu, and T. Wik, “Load-responsive model switching estimation for state of charge of lithium-ion batteries,” Appl. Energy, vol. 238, pp. 423–434, 2019, doi: 10.1016/j.apenergy.2019.01.057.
S. Mian Qaisar, “A Proficient Li-Ion Battery State of Charge Estimation Based on Event-Driven Processing,” J. Electr. Eng. Technol., vol. 15, no. 4, pp. 1871–1877, Jul. 2020, doi: 10.1007/s42835-020-00458-x.
T. Zahid, K. Xu, W. Li, C. Li, and H. Li, “State of charge estimation for electric vehicle power battery using advanced machine learning algorithm under diversified drive cycles,” Energy, vol. 162, pp. 871–882, 2018, doi: 10.1016/j.energy.2018.08.071.
X. Liu, W. Ai, M. Naylor Marlow, Y. Patel, and B. Wu, “The effect of cell-to-cell variations and thermal gradients on the performance and degradation of lithium-ion battery packs,” Appl. Energy, vol. 248, pp. 489–499, 2019, doi: 10.1016/j.apenergy.2019.04.108.
J. Xie, J. Ma, and K. Bai, “Enhanced coulomb counting method for state-of-charge estimation of lithium-ion batteries based on peukert’s law and coulombic efficiency,” J. Power Electron., vol. 18, no. 3, pp. 910–922, 2018, doi: 10.6113/JPE.2018.18.3.910.
M. Fasahat and M. Manthouri, “State of charge estimation of lithium-ion batteries using hybrid autoencoder and Long Short Term Memory neural networks,” J. Power Sources, vol. 469, p. 228375, Sep. 2020, doi: 10.1016/J.JPOWSOUR.2020.228375.
X. Han, L. Lu, Y. Zheng, X. Feng, and Z. Li, “A review on the key issues of the lithium ion battery degradation among the whole life cycle,” eTransportation, vol. 1. 2019. doi: 10.1016/j.etran.2019.100005.
F. Yang, D. Wang, Y. Zhao, K. L. Tsui, and S. J. Bae, “A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries,” Energy, vol. 145, pp. 486–495, 2018, doi: 10.1016/j.energy.2017.12.144.
B. Rente et al., “Lithium-Ion Battery State-of-Charge Estimator Based on FBG-Based Strain Sensor and Employing Machine Learning,” IEEE Sens. J., vol. 21, no. 2, pp. 1453–1460, 2021, doi: 10.1109/JSEN.2020.3016080.
L. Li, C. Wang, S. Yan, and W. Zhao, “A combination state of charge estimation method for ternary polymer lithium battery considering temperature influence,” J. Power Sources, vol. 484, p. 229204, Feb. 2021, doi: 10.1016/J.JPOWSOUR.2020.229204.
S. Zhang, X. Guo, X. Dou, and X. Zhang, “A data-driven coulomb counting method for state of charge calibration and estimation of lithium-ion battery,” Sustain. Energy Technol. Assessments, vol. 40, p. 100752, Aug. 2020, doi: 10.1016/J.SETA.2020.100752.
Y. Zhang et al., “Performance assessment of retired EV battery modules for echelon use,” Energy, vol. 193, p. 116555, Feb. 2020, doi: 10.1016/J.ENERGY.2019.116555.
Z. Huang, F. Yang, F. Xu, X. Song, and K. L. Tsui, “Convolutional Gated Recurrent Unit-Recurrent Neural Network for State-of-Charge Estimation of Lithium-Ion Batteries,” IEEE Access, vol. 7, pp. 93139–93149, 2019, doi: 10.1109/ACCESS.2019.2928037.
A. Yang et al., “A comprehensive investigation of lithium-ion battery degradation performance at different discharge rates,” J. Power Sources, vol. 443, p. 227108, Dec. 2019, doi: 10.1016/j.jpowsour.2019.227108.
H. Huang et al., “A comprehensively optimized lithium-ion battery state-of-health estimator based on Local Coulomb Counting Curve,” Appl. Energy, vol. 322, p. 119469, 2022, doi: 10.1016/j.apenergy.2022.119469.
D. Lee et al., “Modeling the effect of the loss of cyclable lithium on the performance degradation of a lithium-ion battery,” Energies, vol. 12, no. 22, p. 4386, Nov. 2019, doi: 10.3390/en12224386.
X. Li, Z. Wang, and L. Zhang, “Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles,” Energy, vol. 174, pp. 33–44, 2019, doi: 10.1016/j.energy.2019.02.147.
J. A. Braun, R. Behmann, D. Schmider, and W. G. Bessler, “State of charge and state of health diagnosis of batteries with voltage-controlled models,” J. Power Sources, vol. 544, p. 231828, Oct. 2022, doi: 10.1016/J.JPOWSOUR.2022.231828.
K. Hidayat, M. C. Hasani, N. A. Mardiyah, and M. Effendy, “Strategi Pengisian Baterai pada Sistem Panel Surya Standalone Berbasis Kontrol PI Multi-Loop,” J. Tek. Elektro, vol. 13, no. 1, pp. 25–33, 2021, doi: 10.15294/jte.v13i1.29765.
J. A. Prakosa, S. Agmal, E. Kurniawan, M. J. Kholili, and C. Astuti, “Performance Evaluation of Two System Models for a MIMO System to Hover the Bicopter Unmanned Aerial Vehicle,” J. Tek. Elektro, vol. 14, no. 1, pp. 29–35, 2022, doi: 10.15294/jte.v14i1.35746.
S. Boulmrharj et al., “Online battery state-of-charge estimation methods in micro-grid systems,” J. Energy Storage, vol. 30, p. 101518, Aug. 2020, doi: 10.1016/J.EST.2020.101518.
M. F. Ng, J. Zhao, Q. Yan, G. J. Conduit, and Z. W. Seh, “Predicting the state of charge and health of batteries using data-driven machine learning,” Nature Machine Intelligence, vol. 2, no. 3. pp. 161–170, 2020. doi: 10.1038/s42256-020-0156-7.
X. Xin, S. L. Wang, C. M. Yu, J. Cong, and J. Coffie-Ken, “A novel state of charge estimation method for ternary lithium batteries based on system function and extended kalman filter,” Int. J. Electrochem. Sci., vol. 15, no. 3, pp. 2226–2242, 2020, doi: 10.20964/2020.03.47.
K. Movassagh, A. Raihan, B. Balasingam, and K. Pattipati, “A critical look at coulomb counting approach for state of charge estimation in batteries,” Energies, vol. 14, no. 14, p. 4074, 2021, doi: 10.3390/en14144074.
J. Tian, R. Xiong, W. Shen, and J. Wang, “A Comparative Study of Fractional Order Models on State of Charge Estimation for Lithium Ion Batteries,” Chinese J. Mech. Eng. (English Ed., vol. 33, no. 1, pp. 1–15, Dec. 2020, doi: 10.1186/S10033-020-00467-X/FIGURES/16.
M. U. Ali, A. Zafar, S. H. Nengroo, S. Hussain, M. J. Alvi, and H. J. Kim, “Towards a smarter battery management system for electric vehicle applications: A critical review of lithium-ion battery state of charge estimation,” Energies, vol. 12, no. 3. p. 446, 2019. doi: 10.3390/en12030446.
K. Liu, X. Hu, Z. Yang, Y. Xie, and S. Feng, “Lithium-ion battery charging management considering economic costs of electrical energy loss and battery degradation,” Energy Convers. Manag., vol. 195, pp. 167–179, 2019, doi: 10.1016/j.enconman.2019.04.065.
Y. Li, K. Li, X. Liu, Y. Wang, and L. Zhang, “Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning,” Appl. Energy, vol. 285, p. 116410, Mar. 2021, doi: 10.1016/j.apenergy.2020.116410.
Y. Gao, J. Jiang, C. Zhang, W. Zhang, and Y. Jiang, “Aging mechanisms under different state-of-charge ranges and the multi-indicators system of state-of-health for lithium-ion battery with Li(NiMnCo)O2 cathode,” J. Power Sources, vol. 400, pp. 641–651, 2018, doi: 10.1016/j.jpowsour.2018.07.018.
S. Li et al., “State-of-charge estimation of lithium-ion batteries in the battery degradation process based on recurrent neural network,” Energies, vol. 14, no. 2, p. 306, Jan. 2021, doi: 10.3390/en14020306.
J. M. Reniers, G. Mulder, and D. A. Howey, “Review and Performance Comparison of Mechanical-Chemical Degradation Models for Lithium-Ion Batteries,” J. Electrochem. Soc., vol. 166, no. 14, pp. A3189–A3200, 2019, doi: 10.1149/2.0281914jes.
A. Sarkar, I. C. Nlebedim, and P. Shrotriya, “Performance degradation due to anodic failure mechanisms in lithium-ion batteries,” J. Power Sources, vol. 502, p. 229145, Aug. 2021, doi: 10.1016/J.JPOWSOUR.2020.229145.
S. L. Wang et al., “An improved coulomb counting method based on dual open-circuit voltage and real-time evaluation of battery dischargeable capacity considering temperature and battery aging,” Int. J. Energy Res., vol. 45, no. 12, pp. 17609–17621, 2021, doi: 10.1002/er.7042.
Z. Lu et al., “A comprehensive experimental study on temperature-dependent performance of lithium-ion battery,” Appl. Therm. Eng., vol. 158, p. 113800, Jul. 2019, doi: 10.1016/J.APPLTHERMALENG.2019.113800.
Y. Abe, N. Hori, and S. Kumagai, “Electrochemical impedance spectroscopy on the performance degradation of LiFePO4/graphite lithium-ion battery due to charge-discharge cycling under different c-rates,” Energies, vol. 12, no. 23, p. 4507, Nov. 2019, doi: 10.3390/en12234507.
Refbacks
- There are currently no refbacks.