This content is not included in your SAE MOBILUS subscription, or you are not logged in.
A Method for Simultaneous State of Charge, Maximum Capacity and Resistance Estimation of a Li-Ion Cell Based on Equivalent Circuit Model
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
Accurate estimation of the State of Charge (SOC), maximum capacity (Qmax) and internal resistance (R0) are essential for efficient battery monitoring, which is an important part of the battery management system. SOC provides information regarding the instantaneous status of the battery system, while Qmax is a key indicator of the long-term State of Health (SOH) of the cell, which represents the abilities of a battery to store energy and retain charge over extended periods. In addition, the internal resistance is also required to predict the peak available power.
Traditional methods use complex models and look-up tables that have high computation requirements and are thus unsuitable for online applications. In this paper, we propose a simple method for simultaneous SOC, Qmax and internal resistance estimation based on a second-order equivalent circuit model (ECM). An Adaptive Unscented Kalman filter (AUKF) is proposed for joint SOC and ECM parameter estimation along with a forgetting-factor based Recursive Least Square (RLS) filter algorithm to estimate the slow varying Qmax. The two methods are implemented together using a simple closed-loop framework, where the estimated values from one estimator are used to update the other and vice versa.
The proposed algorithms are validated for a large format NMC/Carbon pouch power cell using multiple charge-discharge cycles considering different aging conditions. The experimental results verify the proposed estimation approach with less than 5% SOC estimation error and less than 3% capacity estimation error for the typical SOC range of 10% to 90%.
CitationGairola, S. and Hu, Y., "A Method for Simultaneous State of Charge, Maximum Capacity and Resistance Estimation of a Li-Ion Cell Based on Equivalent Circuit Model," SAE Technical Paper 2020-01-1182, 2020, https://doi.org/10.4271/2020-01-1182.
Data Sets - Support Documents
|[Unnamed Dataset 1]|
|[Unnamed Dataset 2]|
|[Unnamed Dataset 3]|
- Farmann, A., Waag, W., Marongiu, A., and Sauer, D.U. , “Critical Review of on-board Capacity Estimation Techniques for Lithium-ion Batteries in Electric and Hybrid Electric Vehicles,” J. Power Sources 281:114-130, May 2015, doi:10.1016/j.jpowsour.2015.01.129.
- He, H., Xiong, R., and Fan, J. , “Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach,” Energies 4(4):582-598, Apr. 2011, doi:10.3390/en4040582.
- Tang, X., Mao, X., Lin, J., and Koch, B. , “Li-ion Battery Parameter Estimation for State of Charge,” Proceedings of the 2011 American Control Conference, 2011, 941-946, 10.1109/ACC.2011.5990963.
- Rahimi-Eichi, H., Baronti, F., and Chow, M. , “Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells,” IEEE Trans. Ind. Electron. 61(4):2053-2061, Apr. 2014, doi:10.1109/TIE.2013.2263774.
- Zou, Y., Hu, X., Ma, H., and Li, S.E. , “Combined State of Charge and State of Health Estimation over Lithium-Ion Battery Cell Cycle Lifespan for Electric Vehicles,” J. Power Sources 273:793-803, Jan. 2015, doi:10.1016/j.jpowsour.2014.09.146.
- Hu, C., Youn, B.D., and Chung, J. , “A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation,” Appl. Energy 92:694-704, Apr. 2012, doi:10.1016/j.apenergy.2011.08.002.
- Xiong, R., He, H., Sun, F., and Zhao, K. , “Evaluation on State of Charge Estimation of Batteries with Adaptive Extended Kalman Filter by Experiment Approach,” IEEE Trans. Veh. Technol. 62(1):108-117, Jan. 2013, doi:10.1109/TVT.2012.2222684.
- Plett, G.L. , “Sigma-point Kalman Filtering for Battery Management Systems of LiPB-based HEV Battery Packs,” J. Power Sources 161(2):1369-1384, Oct. 2006, doi:10.1016/j.jpowsour.2006.06.004.
- Andre, D., Appel, C., Soczka-Guth, T., and Sauer, D.U. , “Advanced Mathematical Methods of SOC and SOH Estimation for Lithium-Ion Batteries,” J. Power Sources 224:20-27, Feb. 2013, doi:10.1016/j.jpowsour.2012.10.001.
- 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,” Int. J. Electr. Power Energy Syst. 62:783-791, Nov. 2014, doi:10.1016/j.ijepes.2014.04.059.
- Li, Y., Wang, C., and Gong, J. , “A Combination Kalman Filter Approach for State of Charge Estimation of Lithium-Ion Battery Considering Model Uncertainty,” Energy 109:933-946, Aug. 2016, doi:10.1016/j.energy.2016.05.047.
- “Battery Test Manual For Electric Vehicles, Revision 3 (Technical Report),” OSTI.GOV, [Online], available at: https://www.osti.gov/biblio/1186745, accessed 20 Feb. 2019.
- Rahmoun, A. and Biechl, H. , “Parameters Identification of Equivalent Circuit Diagrams for Li-Ion Batteries,” p. 5.
- Willems, J.C., Rapisarda, P., Markovsky, I., and De Moor, B.L.M. , “A Note on Persistency of Excitation,” Syst. Control Lett. 54(4):325-329, Apr. 2005, doi:10.1016/j.sysconle.2004.09.003.
- Glad, S.T. and Ljung, L. , “Model Structure Identifiability and Persistence of Excitation,” in 29th IEEE Conference on Decision and Control, Vol. 6, 1990, 3236-3240, 10.1109/CDC.1990.203389.
- Anderson, B.D.O. , “Adaptive Systems, Lack of Persistency of Excitation and Bursting Phenomena,” Automatica 21(3):247-258, May 1985, doi:10.1016/0005-1098(85)90058-5.
- Alavi, S.M.M., Mahdi, A., Jacob, P.E., Payne, S.J., and Howey, D.A. , “Structural Identifiability Analysis of Fractional Order Models with Applications in Battery Systems,” ArXiv151101402 Math, Nov. 2015.