[논문]배터리 팩 내부 과방전 사전 진단을 위한 모델기반 셀 간 불균형 특성 파라미터 분석 연구

박진형; 김재원; 이미영; 김병철; 정성철; 김종훈

doi:10.6113/tkpe.2021.26.6.381

배터리 팩 내부 과방전 사전 진단을 위한 모델기반 셀 간 불균형 특성 파라미터 분석 연구
Model-based Analysis of Cell-to-Cell Imbalance Characteristic Parameters in the Battery Pack for Fault Diagnosis and Over-discharge Prognosis 원문보기

전력전자학회 논문지 = The Transactions of the Korean Institute of Power Electronics, v.26 no.6, 2021년, pp.381 - 389

박진형 (Dept. of Electrical Engineering, Chungnam National University) , 김재원 (Dept. of Electrical Engineering, Chungnam National University) , 이미영 (Dept. of Electrical Engineering, Chungnam National University) , 김병철 (Power Grid Integration Team Power & Industrial System R&D Center, Hyosung Corporation) , 정성철 (Power Grid Integration Team Power & Industrial System R&D Center, Hyosung Corporation) , 김종훈 (Dept. of Electrical Engineering, Chungnam National University)

Abstract ▼ AI-Helper

Most diagnosis approaches rely on historical failure data that might not be feasible in real operating conditions because the battery voltage and internal parameters are nonlinear according to various operating conditions, such as cell-to-cell configuration and initial condition. To overcome this issue, the estimator and the predictor require integrated approaches that consider comprehensive data, with the degradation process and measured data taken into account. In this paper, vector autoregressive models (VAR) with various parameters that affect overdischarge to the cell in the battery pack were constructed, and the cell-to-cell parameters were identified using an adaptive model to analyze the influence of failure prognosis. The theoretical analysis is validated using experimental results in terms of the feasibility and advantages of fault prognosis.

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표/그림 (11)

그림 Fig. 1. Intersection requirement of prediction model.
그림 Fig. 2. Electrical equivalent circuit model.
그림 Fig. 3. Estimation result of series resistance using MAFF-RLS comparing with fault and normal cell.
그림 Fig. 4. Estimation result of open circuit voltage using MAFF-RLS comparing with fault and normal cell.
그림 Fig. 5. Estimation result of diffusion resistance using MAFF-RLS comparing with fault and normal cell.
그림 Fig. 6. Experimental result of terminal voltage using MAFF-RLS comparing with fault and normal cell.
그림 Fig. 7. Parameter tendency in the over-discharge region comparing with fault and normal cell.
그림 Fig. 8. Prediction result of the conventional AR model.
그림 Fig. 9. Comparison of the various VAR model in over-discharge region.
표 TABLE I MEAN ABSOLUTE PERCENTAGE ERROR RESULTS OF AR AND VAR
그림 Fig. 10. Enlarged result of the voltage prediction in over-discharge region.

참고문헌 (34)

IRENA, "Electricity storage and renewables: costs and VARkets to 2030," International Renewable Energy Agency, Abu Dhabi, 2017.
IEA, "Understanding the world energy outlook scenarios," 2019.
IRENA, "Global energy transformation: A roadmap to 2050," 2019 ed., Apr. 2019.
Bloomberg New Energy Finance, "Energy storage outlook 2019," BloombergNEF, Paris, France, 2019.
B. Dunn, H. Kamath, and J. M. Tarascon, "Electrical energy storage for the grid: A battery of choices," Science, Vol. 334, No. 6058, pp. 928-935, Nov. 2011.

상세보기
W. Jang, "Crisis in the domestic ESS industry environment - causes and action," VIP Report, Vol. 734, pp. 1-19, 2020.
Ministry of Trade, Industry and Energy, "ESS fire accident investigation result, public-private partnership ESS fire accident investigation result," Jun. 2019.
KESCO & KTI, "ESS fire accident investigation result report, ESS fire accident investigation team," Feb. 2020.
R. Xiong, W. Sun, Q. Yu, and F. Sun, "Research progress, challenges and prospects of fault diagnosis on battery system of electric vehicles," Applied Energy, Vol. 279, 115855, Dec. 2020.

상세보기
M. F. Samadi and M. Saif, "Integrated battery, management system," Integrated Systems: Innovations and Applications, Springer, Cham, pp. 173-193, 2015.
Y. Xing, E. W. Ma, K. L. Tsui, and M. Pecht, "Battery management systems in electric and hybrid vehicles," Energies, Vol. 4, No. 11, pp. 1840-1857, Oct. 2011.
M. A. Hannan, M. H. Lipu, A. Hussain, and A. Mohamed, "A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations," Renewable and Sustainable Energy Reviews, Vol. 78, pp. 834-854, Oct. 2017.

상세보기
R. Xiong, J. Cao, Q. Yu, H. He, and F. Sun, "Critical review on the battery state of charge estimation methods for electric vehicles," IEEE Access, Vol. 6, pp. 1832-1843, Dec. 2017.

상세보기
X. Hu et al., "Advanced fault diagnosis for lithium-ion battery systems," IEEE Industrial Electronics Magazine, Vol. 14, No. 3, pp. 65-91, Sep. 2020.

상세보기
J. Xiong et al., "Failure detection for over-discharged Li-ion batteries," in Proceedings of 2012 IEEE International Electric Vehicle Conference, Greenville, SC, pp. 1-5, Mar. 2012.
V. K. Muddappa and S. Anwar, "Electrochemical model based fault diagnosis of Li-ion battery using fuzzy logic," in ASME 2014 International Mechanical Engineering Congress and Exposition, pp. 9, Mar. 2015.
J. Huber et al., "Method for classification of battery separator defects using optical inspection," Procedia CIRP, Vol. 57, pp. 585-590, Nov. 2016.

상세보기
X. Feng, C. Weng, M. Ouyang, and J. Sun, "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Vol. 161, pp. 168 -180, Jan. 2016.

상세보기
A. Singh, A. Izadian, and S. Anwar, "Fault diagnosis of Li-ion batteries using multiple-model adaptive estimation," in IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society, Vienna, Austria, pp. 3524-3529, Nov. 2013.
M. Ouyang et al., "Internal short circuit detection for battery pack using equivalent parameter and consistency method," Journal of Power Sources, Vol. 294, pp. 272-283, 2015.

상세보기
X. Feng et al., "Online internal short circuit detection for a large format lithium ion battery," Applied Energy, Vol. 161, pp. 168-180, Jan. 2016.

상세보기
Y. Zhao, P. Liu, Z. Wang, L. Zhang, and J. Hong, "Fault and defect diagnosis of battery for electric vehicles based on big data analysis methods," Applied Energy, Vol. 207, pp. 354-362, Dec. 2017.

상세보기
E. Meissner and G. Richter, "Battery monitoring and electrical energy management: precondition for future vehicle electric power systems," Journal of Power Sources, Vol. 116, pp. 79-98, 2003.

상세보기
L. Zhou et al., "A study on parameter variation effects on battery packs for electric vehicles," Journal of Power Sources, Vol. 364, pp. 242-252, 2017.

상세보기
B. Wu, V. Yufit, M. Marinescu, G. J. Offer, R. F. Martinez-Botas, and N. P. Brandon, "Coupled thermal-electrochemical modelling of uneven heat generation in lithium-ion battery packs," Journal of Power Sources, Vol. 243, pp. 544-554. 2013.

상세보기
C. Fleischer, W. Waag, Z. Bai, and D. Sauer, "On-line self-learning time forward voltage prognosis for lithium-ion batteries using adaptive neuro-fuzzy inference system," Journal of Power Sources, Vol. 243, 728e49, 2013.

상세보기
X. Zhang, Y. Wang, J. Wu, and Z. Chen, "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Vol. 216, pp. 442-451, Apr. 2018.

상세보기
H. F. Dai, X. Z. Wei, and Z. C. Sun, "Recursive parameter identification of lithium-ion battery for EVs based on equivalent circuit model," Journal of Computational and Theoretical Nanoscience, Vol. 10, pp. 2813-2818, 2013.

상세보기
A. Vahidi, A. Stefanopoulou, and H. Peng, "Recursive least squares with forgetting for online estimation of vehicle mass and road grade: theory and experiments," Vehicle System Dynamics, Vol. 43, No. 1, pp. 31-55, 2005.

상세보기
B. D. Ketelaere, H. Saelens, L. Wulteputte, and J. Anthonis, "Nozzle fault detection using recursive least-squares," in 20th Annual Conference on Liquid Atomization and Spray Systems, Chicago, USA, 2007.
V. H. Duong, H. A. Bastawrous, K. Lim, K. W. See, P. Zhang, and S. X. Dou, "Online state of charge and model parameters estimation of the LiFePO 4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares," Journal of Power Sources, Vol. 296, pp. 215-224, 2015.

상세보기
J. Park, M. Lee, G. Kim, S. Park, and J. Kim, "Integrated approach based on dual extended kalman filter and multivariate autoregressive model for predicting battery capacity using health indicator and SOC/SOH," Energies, Vol. 13, No. 9, pp. 2138, 2020.

상세보기
K. Moon, "Vector autoregressive model," KOSTAT, Vol. 2.1, pp. 23-56, 1997.
G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time series analysis: Forecasting and control, John Wiley & Sons, 2008.

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