ISSN: 0304-128X ISSN: 2233-9558
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Received December 15, 2021
Accepted January 24, 2022
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확장칼만필터를 활용한 배터리 시스템에서의 State of Charge와 용량 동시 추정

Simultaneous Estimation of State of Charge and Capacity using Extended Kalman Filter in Battery Systems

광운대학교 화학공학과, 01897 서울시 노원구 광운로 20
Department of Chemical Engineering, Kwangwoon University, 20 Kwangwoon-ro,Nowon-gu, Seoul, 01897, Korea
Korean Chemical Engineering Research, August 2022, 60(3), 363-370(8), 10.9713/kcer.2022.60.3.363 Epub 18 July 2022
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Abstract

본 논문에서는 전기자동차용 배터리 충/방전 상태 추정의 정확도를 개선하기 위해 칼만 필터(Kalman Filter, KF) 알 고리즘과 등가회로모델(Equivalent Circuit Model)을 활용한 State Of Charge (SOC) 추정 방법을 적용하였다. 특히 노 화된 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 일어나 지는 않다는 점에 착안하여, 충전 시 배터리 용량 추정을 주기적으로 실시하는 전략을 제시하였다. 충전 모드에서는 일 정 구간마다 전류가 고정되기에, 해당 상황에서 배터리 노화 용량을 SOC와 함께 추정 전략을 제시하였다. 전류가 고 정된 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. 이 를 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.
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.

References

Ouyang Q, Chen J, Zheng J, IEEE Trans. PowerPower Electr., 35(6), 5820 (2019)
Ryu K, Kim B, Kim D,, Jang M, Ko H, Kim H, J. Korea Academia-Industrial Cooperation Society, 18(10), 15 (2017)
Xiong R et al., IEEE Access, 6, 1832 (2017)
Ouyang Q, Chen J, You K, “State of Charge Estimation of Lithium-ion Batteries with Unknown Model Parameters, 2016 American Control Conference (ACC), IEEE, 2016
Kai, Wang, et al., Int. J. Electrochem. Sci., 15(9), 9499 (2020)
Bae KC, et al., The Korean Institute of Power Electronics, 528 (2014)
Koo J, Study on SoC Accuracy Improvement of Lithium-Ion Battery System, Graduate School of Chonnam National University (2019).
Jang K, Jeong K, Transactions of the Korean Institute of Power Electronics, 17(3) (2012)
Hannan MA, Lipu MSH, Hussain A, Saad MH, Ayob A, IEEE Access, 6, 10069 (2018)
Tong S, Lacap JH, Park JW, J. Energy Storage, 7, 236 (2016)
He W, Williard N, Chen C, Pecht M, Int. J. Electrical Power Energy Systems, 62, 783 (2014)
Berecibar M, Gandiaga I, Villarreal I, Omar N, Van Mierlo J, Van den Bossche P, Renew. Sust. Energ. Rev., 56, 572 (2016)
Duo Y, Yujie W, Rui P, Ruiyang C, Zonghai C, Energy Procedia, 105, 2059 (2017)
Shen P, Ouyang M, Lu L, Li J, Feng X, IEEE Trans. Vehicular Technol., 67(1), 92 (2018)
Jo S, Jung S, Kim H, J. Korea Convergence Soc., 11(6), 7 (2020)
How DN, Hannan MA, Lipu MS, Ker PJ, IEEE Access, 7, 136116 (2019)
Simon D, Optimal state estimation: Kalman, H infinity, and nonlinear approaches, John Wiley & Sons, 2006.
Sin S, Park J, Baek J, Kang M, Kim J, Power Electronics Conference, 11, 230 (2020)
Lee S, Park M, J. IKEEE, 18(3), 298 (2014)
Steven MK, Fundamentals of Statistical Signal Processing, Pearson Education, 2013.
Chen CT, Linear System Theory and Design, 4ed, Oxford University Press, 2013.
Krener AJ, Ide K, “Measures of Unobservability,” Proceedings of the 48h IEEE Conference on Decision and Control (CDC), 6401-6406(2009).
Manitsas E, Singh R, Pal BC, Strbac G, IEEE Trans. Power Systems, 27(4), 1888 (2012)
Wu J, He Y, Jenkins N, IEEE Trans. Power Systems, 28(2), 1008 (2013)
Rey D, Chaos, observability and symplectic structure in optimal estimation. UC San Diego Ph.D. Thesis, 2017.
Singh R, Pal BC, Vinter RB, IEEE Trans. Power Systems, 24(2), 668 (2009)
Bhela S, Kekatos V, Veeramachaneni S, IEEE Trans. Smart Grid, 9(6), 5953 (2018)
Jiang H, Zhang Y, IEEE Power Energy Society General Meeting, 1 (2016)

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