2024

Vol.31 No.3

Current Issue Archives
Search
Advanced Search
Editorial Office

Review

  • Journal of the Microelectronics and Packaging Society
  • Volume 31(1); 2024
  • Article

Review

Journal of the Microelectronics and Packaging Society 2024;31(1):16-22. Published online: May, 10, 2024

PDF Full Text XML

DOI : doi.org/10.6117/kmeps.2024.31.1.016

State of Health and State of Charge Estimation of Li-ion Battery for Construction Equipment based on Dual Extended Kalman Filter

  • Hong-Ryun Jung1 , Jun Ho Kim1 , Seung Woo Kim1 , Jong Hoon Kim2 , Eun Jin Kang2 , and Jeong Woo Yun1,†
    1School of Chemical Engineering, Chonnam National University, Gwangju 61186, Korea, 2Department of Electrical Engineering, Chungnam National University 34134, Korea
Corresponding author E-mail: jwyun@jnu.ac.kr
Abstract

Along with the high interest in electric vehicles and new renewable energy, there is a growing demand to apply lithium-ion batteries in the construction equipment industry. The capacity of heavy construction equipment that performs various tasks at construction sites is rapidly decreasing. Therefore, it is essential to accurately predict the state of batteries such as SOC (State of Charge) and SOH (State of Health). In this paper, the errors between actual electrochemical measurement data and estimated data were compared using the Dual Extended Kalman Filter (DEKF) algorithm that can estimate SOC and SOH at the same time. The prediction of battery charge state was analyzed by measuring OCV at SOC 5% intervals under 0.2C-rate conditions after the battery cell was fully charged, and the degradation state of the battery was predicted after 50 cycles of aging tests under various C-rate (0.2, 0.3, 0.5, 1.0, 1.5C rate) conditions. It was confirmed that the SOC and SOH estimation errors using DEKF tended to increase as the C-rate increased. It was confirmed that the SOC estimation using DEKF showed less than 6% at 0.2, 0.5, and 1C-rate. In addition, it was confirmed that the SOH estimation results showed good performance within the maximum error of 1.0% and 1.3% at 0.2 and 0.3C-rate, respectively. Also, it was confirmed that the estimation error also increased from 1.5% to 2% as the C-rate increased from 0.5 to 1.5C-rate. However, this result shows that all SOH estimation results using DEKF were excellent within about 2%.

Keywords Lithium-ion battery, Construction equipment, Kalman filter, Equivalent circuit model, State of Health (SOH)

REFERENCES
  • P. Keil, S. Schuster, C. Luders, H. Hesse, A. Arunachala, and A. Jossen, "Lifetime Analyses of Lithium-Ion EV Batteries", 3rd Electromobility Challenging Issues conference (ECI), 1-5 (2015).
  • D. Yan, L. Lu, Z. Li, X. Feng, M. Ouyang, and F. Jiang, "Durability comparison of four different types of high-power batteries in HEV and their degradation mechanism analysis", Appl. Energ., 179, 1123-1130 (2016). https://doi.org/10.1016/j.apenergy.2016.07.054
  • N. A. Azis , E. Joelianto, and A. Widyotriatmo, "State of Charge (SoC) and State of Health (SoH) Estimation of Lithium-Ion Battery Using Dual Extended Kalman Filter Based on Polynomial Battery Model", In 2019 6th International Conference on Instrumentation, Control, and Automation (ICA), 8916734 (2019).
  • M. Cacciato, G. Nobile, G. Scarcella, and G. Scelba, "Real-time model-based estimation of SOC and SOH for energy storage systems", IEEE T. Power Electr., 32(1), 794-803 (2016).
  • J. Xu, M. Gao, Z. He, Q. Han, and X. Wang, "State of charge estimation online based on EKF-Ah method for lithium-ion power battery", In 2009 2nd International Congress on Image and Signal Processing, IEEE, 5303451 (2009).
  • N. Wassiliadis, J. Adermann, A. Frericks, M. Pak, C. Reiter, B. Lohmann, and M. Lienkamp, "Revisiting the dual extended Kalman filter for battery state-of-charge and state-of-health estimation: A use-case life cycle analysis", Journal of Energy Storage, 19, 73-87 (2018).
  • C. Rossi, C. Falcomer, L. Biondani, and D. Pontara, "Genetically optimized extended Kalman filter for state of health estimation based on Li-ion batteries parameters", Energies, 15(9), 3404 (2022)
  • M. S. H. Lipu, M. A. Hannan, A. Hussain, M. M. Hoque, P. J. Ker, M. H. M. Saad, and A. Ayob, "A review of state of health and remaining useful life estimation methods for lithium-ion battery in electric vehicles: Challenges and recommendations", J. Clean. Prod., 205, 115-133 (2018). https://doi.org/10.1016/j.jclepro.2018.09.065
  • Z. Lyu, and R. Gao, "A model-based and data-driven joint method for state-of-health estimation of lithium-ion battery in electric vehicles", Int. J. Energ. Res. 43(14), 7956-7969 (2019).
  • P. Y. Lee, and J. Kim, "Impact analysis of deterioration and SOH estimation based on multiple regression analysis", In 2019 IEEE Transportation Electrification Conference and Expo, Asia-Pacific (ITEC Asia-Pacific), 8903656 (2019).
  • L. Fang, J. Li, and B. Peng, "Online Estimation and Error Analysis of both SOC and SOH of Lithium-ion Battery based on DEKF Method", Energy Proced., 158, 3008-3013 (2019). https://doi.org/10.1016/j.egypro.2019.01.974
  • G. W. Kim, J. H. Park, M. O. Kim, and J. H. Kim, "Study on analysis of SOH estimation tendency according to C-rate of Li-ion battery using DEKF", The Korean Institute of Power Electronics, In Proceedings of the KIPE Conference, 194-195 (2019)
  • J. Park, M. Lee, G. K im, S. Park, and J. K im, "Integrated approach based on dual extended Kalman filter and multivariate autoregressive model for predicting battery capacity using health indicator and SOC/SOH", Energies, 13(9), 2138 (2020).
  • M. I. Ribeiro, "Kalman and Extended Kalman Filters: Concept, Derivation and Properties", Institute for Systems and Robotics, 43(46), 3736-3741 (2004).
  • J. Kim, "Discrete wavelet transform-based feature extraction of experimental voltage signal for Li-ion cell consistency", IEEE T. Veh. Technol., 65(3), 1150-1161 (2016). https://doi.org/10.1109/TVT.2015.2414936
  • V. Muenzel, A. F. Hollenkamp, A. I. Bhatt, J. de Hoog, M. Brazil, D. A. Thomas, and I. Mareels, "A comparative testing study of commercial 18650-format lithium-ion battery cells", J. Electrochem. Soc., 162(8), A1592 (2015).
  • H. Bindner, T. Cronin, P. Lundsager, J. F. Manwell, U. Abdulwahid, and I. Baring-Gould, "Lifetime modelling of lead acid batteries", Riso National Laboratory, Denmark (2005).