Korean Chemical Engineering Research

The Korean Institute of Chemical Engineers (한국화학공학회)
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Simultaneous Estimation of State of Charge and Capacity using Extended Kalman Filter in Battery Systems

확장칼만필터를 활용한 배터리 시스템에서의 State of Charge와 용량 동시 추정

  • Mun, Yejin (Department of Chemical Engineering, Kwangwoon University) ;
  • Kim, Namhoon (Department of Chemical Engineering, Kwangwoon University) ;
  • Ryu, Jihoon (Department of Chemical Engineering, Kwangwoon University) ;
  • Lee, Kyungmin (Department of Chemical Engineering, Kwangwoon University) ;
  • Lee, Jonghyeok (Department of Chemical Engineering, Kwangwoon University) ;
  • Cho, Wonhee (Department of Chemical Engineering, Kwangwoon University) ;
  • Kim, Yeonsoo (Department of Chemical Engineering, Kwangwoon University)
  • 문예진 (광운대학교 화학공학과) ;
  • 김남훈 (광운대학교 화학공학과) ;
  • 유지훈 (광운대학교 화학공학과) ;
  • 이경민 (광운대학교 화학공학과) ;
  • 이종혁 (광운대학교 화학공학과) ;
  • 조원희 (광운대학교 화학공학과) ;
  • 김연수 (광운대학교 화학공학과)
  • Received : 2021.12.15
  • Accepted : 2022.01.24
  • Published : 2022.08.01
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Abstract

In this paper, an estimation algorithm for state of charge (SOC) was applied using an equivalent circuit model (ECM) and an Extended Kalman Filter (EKF) to improve the estimation accuracy of the battery system states. In particular, an observer was designed to estimate SOC along with the aged capacity. In the case of the fresh battery, when SOC was estimated by Kalman Filter (KF), the mean absolute percentage error (MAPE) was 0.27% which was smaller than MAPE of 1.43% when the SOC was calculated by the model without the observer. In the driving mode of the vehicle, the general KF or EKF algorithm cannot be used to estimate both SOC and capacity. Considering that the battery aging does not occur in a short period of time, a strategy of periodically estimating the battery capacity during charging was proposed. In the charging mode, since the current is fixed at some intervals, a strategy for estimating the capacity along with the SOC in this situation was suggested. When the current was fixed, MAPE of SOC estimation was 0.54%, and the MAPE of capacity estimation was 2.24%. Since the current is fixed when charging, it is feasible to estimate the battery capacity and SOC simultaneously using the general EKF. This method can be used to periodically perform battery capacity correction when charging the battery. When driving, the SOC can be estimated using EKF with the corrected capacity.

본 논문에서는 전기자동차용 배터리 충/방전 상태 추정의 정확도를 개선하기 위해 칼만 필터(Kalman Filter, KF) 알고리즘과 등가회로모델(Equivalent Circuit Model)을 활용한 State Of Charge (SOC) 추정 방법을 적용하였다. 특히 노화된 배터리 용량을 함께 추정 가능한 관측기(observer)를 설계하였다. 우선 노화가 없는 경우, 칼만 필터를 이용하여 SOC를 단일 추정하면, 관측기 없이 모델로 계산된 경우와 비교하여 평균 절대 오차율이 1.43%(관측기 미사용)에서 0.27%(관측기 사용)로 감소하였다. 차량 주행상태에서는 전류가 고정되지 않아 SOC와 배터리 용량을 모두 추정하는 것에 일반적인 KF 혹은 Extended KF 알고리즘을 이용할 수 없다. 배터리 노화에 의한 용량 변화는 단시간에 일어나지는 않다는 점에 착안하여, 충전 시 배터리 용량 추정을 주기적으로 실시하는 전략을 제시하였다. 충전 모드에서는 일정 구간마다 전류가 고정되기에, 해당 상황에서 배터리 노화 용량을 SOC와 함께 추정 전략을 제시하였다. 전류가 고정된 상태에서 SOC 추정의 평균 절대 오차율은 0.54% 였으며, 용량 추정의 평균 절대 오차율은 2.24%로 나타났다. 충전상태에서 전류가 고정됨으로 일반적인 EKF를 활용하여 배터리 용량과 SOC 동시 추정이 가능하도록 하였다. 이를 통하여 배터리 충전 시 주기적인 배터리 용량 보정을 수행할 수 있다. 그리고, 방전 시에는 해당 용량으로 고정한 채 SOC를 추정하는, 배터리 관리 시스템에서 활용 가능한 추정 알고리즘을 제안하였다.

Keywords

References

  1. Ouyang, Q., Chen, J. and Zheng, J., "State-of-charge Observer Design for Batteries with Online Model Parameter Identification: A Robust Approach," IEEE Transactions on Power Electronics, 35(6), 5820-5831(2019). https://doi.org/10.1109/tpel.2019.2948253
  2. Ryu, K., Kim, B., Kim, D., Jang, M., Ko, H. and Kim, H., "A State-of-Charge Estimation Using Extended Kalman Filter for Battery of Electric Vehicle," Journal of the Korea Academia-Industrial Cooperation Society, 18(10), 15-23(2017). https://doi.org/10.5762/KAIS.2017.18.10.15
  3. Xiong Rui, et al. "Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles," IEEE Access, 6, 1832-1843(2017). https://doi.org/10.1109/access.2017.2780258
  4. Ouyang, Q., Chen, J. and You, K., "State of Charge Estimation of Lithium-ion Batteries with Unknown Model Parameters, 2016 American Control Conference (ACC), IEEE, 2016.
  5. Kai, Wang, et al. "State of Charge (SOC) Estimation of Lithiumion Battery Based on Adaptive Square Root Unscented Kalman Filter," International Journal of Electrochemical Science, 15(9), 9499-9516 (2020). https://doi.org/10.20964/2020.09.84
  6. Bae, K.-C., et al. "OCV Prediction Method for SOC Estimation of Li-ion Battery. In: Proceedings of the KIPE Conference," The Korean Institute of Power Electronics, 528-529(2014).
  7. Koo, J., Study on SoC Accuracy Improvement of Lithium-Ion Battery System, Graduate School of Chonnam National University(2019).
  8. Jang, K. and Jeong, K., "A SOC Estimation using Kalman Filter for Lithium-Polymer Battery," Transactions of the Korean Institute of Power Electronics, 17(3), (2012).
  9. Hannan, M. A., Lipu, M. S. H., Hussain, A., Saad, M. H. and Ayob, A., "Neural Network Approach for Estimating State of Charge of Lithium-Ion Battery Using Backtracking Search Algorithm," IEEE Access, 6, 10069-10079 (2018). https://doi.org/10.1109/access.2018.2797976
  10. Tong, S., Lacap, J. H., Park, J. W., "Battery State of Charge Estimation Using a Load-classifying Neural Network," Journal of Energy Storage, 7, 236-243(2016). https://doi.org/10.1016/j.est.2016.07.002
  11. He, W., Williard, N., Chen, C. and Pecht, M., "State of Charge Estimation for Li-ion Batteries Using Neural Network Modeling and Unscented Kalman Filter-based Error Cancellation," International Journal of Electrical Power & Energy Systems, 62, 783-791(2014). https://doi.org/10.1016/j.ijepes.2014.04.059
  12. Berecibar, M., Gandiaga, I., Villarreal, I., Omar, N., Van Mierlo, J. and Van den Bossche, P., "Critical Review of State of Health Estimation Methods of Li-ion Batteries for Real Applications," Renewable and Sustainable Energy Reviews, 56, 572-587(2016). https://doi.org/10.1016/j.rser.2015.11.042
  13. Duo Yang, Yujie Wang, Rui Pan, Ruiyang Chen, Zonghai Chen, "A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles," Energy Procedia, 105, 2059-2064(2017). https://doi.org/10.1016/j.egypro.2017.03.583
  14. Shen, P., Ouyang, M., Lu, L., Li, J. and Feng, X., "The Co-estimation of State of Charge, State of Health, and State of Function for Lithium-Ion Batteries in Electric Vehicles," IEEE Transactions on Vehicular Technology, 67(1), 92-103(2018). https://doi.org/10.1109/tvt.2017.2751613
  15. Jo, S., Jung, S. and Kim, H., "Development of Battery Monitoring System Using the Extended Kalman Filter," Journal of the Korea Convergence Society, 11(6), 7-14(2020). https://doi.org/10.15207/JKCS.2020.11.6.007
  16. How, D. N., Hannan, M. A., Lipu, M. S. and Ker, P. J., "State of Charge Estimation for Lithium-ion Batteries Using Model-based and Data-driven Methods: A Review," IEEE Access, 7, 136116-136136(2019). https://doi.org/10.1109/access.2019.2942213
  17. Simon, D., Optimal state estimation: Kalman, H infinity, and nonlinear approaches, 2006: John Wiley & Sons.
  18. Sin, S., Park, J., Baek, J., Kang, M. and Kim, J., "Improving Accuracy of Battery Modeling by Estimating the Diffustion Voltage of Li-ion Batteries Based on EKF," Power Electronics Conference, 11, 230-231(2020).
  19. Lee, S. and Park, M., "Battery State of Charge Estimation Considering the Battery Aging," Journal of IKEEE, 18(3), 298-304(2014). https://doi.org/10.7471/IKEEE.2014.18.3.298
  20. Steven M. Kay, Fundamentals of Statistical Signal Processing, 2013: Pearson Education.
  21. Chen, C.-T., Linear System Theory and Design, 4ed, 2013:Oxford University Press.
  22. Krener, A. J. and Ide, K., "Measures of Unobservability," Proceedings of the 48h IEEE Conference on Decision and Control (CDC), 6401-6406(2009).
  23. Manitsas, E., Singh, R., Pal, B. C. and Strbac, G., "Distribution System State Estimation Using An Artificial Neural Network Approach for Pseudo Measurement Modeling," IEEE Transactions on Power Systems, 27(4), 1888-1896(2012). https://doi.org/10.1109/TPWRS.2012.2187804
  24. Wu, J., He, Y. and Jenkins, N., "A Robust State Estimator for Medium Voltage Distribution Networks," IEEE Transactions on Power Systems, 28(2), 1008-1016(2013). https://doi.org/10.1109/TPWRS.2012.2215927
  25. Rey, D., Chaos, observability and symplectic structure in optimal estimation. UC San Diego Ph.D. Thesis, 2017.
  26. Singh, R., Pal, B. C. and Vinter, R. B., "Measurement Placement in Distribution System State Estimation," IEEE Transactions on Power Systems, 24(2), 668-675(2009). https://doi.org/10.1109/tpwrs.2009.2016457
  27. Bhela, S., Kekatos, V. and Veeramachaneni, S., "Enhancing Observability in Distribution Grids Using Smart Meter Data," IEEE Transactions on Smart Grid, 9(6), 5953-5961(2018). https://doi.org/10.1109/tsg.2017.2699939
  28. Jiang, H. and Zhang, Y., "Short-term Distribution System State Forecast Based on Optimal Synchrophasor Sensor Placement and Extreme Learning Machine," IEEE Power and Energy Society General Meeting (PESGM), 1-5(2016).