Joint Estimation for Battery Capacity and the State of Charge Based on Variable Time Scale

Document Type : Research Article


1 School of Vehicle Engineering, Xi’an Aeronautical Institute, Xi’an, P.R. CHINA

2 Transportation Industry Key Laboratory of Automobile Transportation Safety Assurance Technology, Chang’an University, Xi’an, P.R. CHINA


As the core energy source of electric vehicles, power batteries directly restrict the development of electric vehicles. Accurate estimation of SOC is not only the fundamental function of the electric vehicle battery management system but also helps to improve energy utilization of batteries, safeguard the application of batteries in EVs, and extend the cycling life. However,    the time-varying nonlinearity, environmental sensitivity, and irreversible decay during the use of the battery make the estimation of hidden states such as SOC a challenge to the industry. This study conducted the following research on the SOC and capacity estimation of lithium-ion batteries: (1)To achieve the co-estimation of the battery’s state and parameters, an adaptive cubature Kalman filter SOC estimation method based on random weighting (ARWCKF) is proposed, at the same time, Extended Kalman Filter (EKF) is used to identify the parameter on-line. The results verify that this approach has a better performance with the error of SOC being under 3%. (2) Aiming at the limitations of the single-time-scale joint estimation algorithm, taking accumulated discharge as the conversion standard between micro and macro time scales. The filtering performance of the algorithm is effectively evaluated based on the prediction accuracy of the terminal voltage, SOC, capacity, and the convergence rate of SOC and capacity, verifying that compared to the single-time-scale approach, this approach has better robustness and accuracy.


Main Subjects

[1] Ma J., Liu X.D, Chen Y.S., The present situation and Countermeasures of the Development of New Energy vehicle Industry and Technology in China. China J. Highway Transport, 31(08): 1-19 (2018).
[2] “The State Council released the Development Plan of Energy Saving and New Energy Automobile Industry” (2012-2020).
[3] Chemali E., Kollmeyer P.J., Preindl M., Emadi A., State-of-charge Estimation of Li-Ion Batteries Using Deep Neural Networks: A Machine Learning Approach, J. Power Sources, 400: 242-255 (2018).
[4] Manane Y., Yazami R., Accurate State of  Charge Assessment of Lithium-Manganese Dioxide Primary Batteries, J. of Power Sources, 359: 422-426 (2017).
[5] Ramadan H.S., Becherif M., Claude F., 2017. Extended Kalman Filter for Accurate State of Charge Estimation of Lithium-Based Batteries: A Comparative Analysis. Int. J. Hydrog. Energy, 42(48): 29033-29046 (2017).
[6] Chen X., Shen W., Cao Z., Kapoor A., 2014. A novel Approach for State of Charge Estimation Based on Adaptive Switching Gain Sliding Mode Observer in Electric Vehicles, J. Power Sources, 246: 667-678 (2014).
[7] Gallardo-Lozano J., Romero-Cadaval E., Milanes-Montero M.I., Guerrero-Martinez M.A., Battery Equalization Active Methods, J. Power Sources, 246: 934-949 (2014).
[8] Khan M.R., Mulder G., Van Mierlo J., An Online Framework for State of Charge Determination of Battery Systems Using Combined System Identification Approach, J. Power Sources, 246: 629-641 (2014).
[9] Moura S.J., Chaturvedi N.A., Krstić M., Adaptive Partial Differential Equation Observer for Battery State-of-Charge/ State-of-Health Estimation via an Electrochemical Model, J. Dyn. Syst., Measur., Cont., 136(1): (2014).
[11] Lee S., Kim J., Lee J., Cho B.H., State-of-Charge and Capacity Estimation of Lithium-Ion Battery Using a New Open-Circuit Voltage Versus State-of-Charge. J. Power Sources, 185(2): 1367-1373 (2008).
[12] Yue Z., Lian B., Tang C., “The Gps/Ins Integrated Navigation Method Based on Adaptive Ssr-Sckf Cubature Kalman Filter”, China Satell. Navigat. Conf., 395-405 (2017).
[13] Arasaratnam I., Haykin S., Cubature Kalman Filters, IEEE Trans. Auto. Cont., 54(6): 1254-1269 (2009).
[14] Zhang H., Xie J., Ge J., Lu W., Liu B., Strong Tracking SCKF Based on Adaptive CS Model for Manoeuvring Aircraft Tracking, IET Radar, Sonar Navig., 12(7): 742-749 (2018).
[15] Hu Y., Zhang S., Luo L. Fault Diagnosis of Gas Circuit Components of Turbofan Engine Based on Adaptive Volume Kalman Filter, Journal of Aerospace Power, 31(5): 1260-1267 (2016).
[16] Xinglong T.A.N., Jian W.A.N.G., Changsheng Z.H.A.O., Neural Network Aided Adaptive UKF Algorithm for GPS/INS Integration Navigation, Acta Geod. Cartogr. Sin., 44(4): 384 (2015).
[17] Gao W.G., He H.B., Chen J.P., 2008. An Adaptive UKF Algorithm and its Application for GPS/INS integrated Navigation System, Trans. Beijing Ins. Technol., 28: 505-509 (2008).
[18] Mahmood A., Wang, J.L., Machine Learning for High Performance Organic Solar Cells: Current Scenario and Future Prospects, Energ. Environ. Sci., 14(1): 90-105 (2021).
[20] Gao S., Zhong Y., Shirinzadeh B., Random Weighting Estimation for Fusion of Multi-Dimensional Position Data, Inf. Sci., 180(24): 4999-5007 (2010).
[21] Gao Z., Mu D., Zhong Y., Gu C., Ren C., Adaptively Random Weighted Cubature Kalman Filter for Nonlinear Systems, Math. Probl. Eng., 2019: (2019)
[23] Cho S., Jeong H., Han C., Jin S., Lim J.H., Oh J., State-of-Charge Estimation for Lithium-Ion Batteries under Various Operating Conditions Using an Equivalent Circuit Model, Comput. Chem. Eng., 41: 1-9 (2012).
[24] Duan Y., “Study on SOC Estimation Method of Lithium-Ion Battery for Electric vehicle”. Ph.D. Thesis, Hunan University, Changsha, China (2018).
[25] Xiong R., “Research on State Estimation of Electric Vehicle Power Battery Pack Based on Data Model Fusion”. PhD, Thesis, Beijing Institute of Technology, Beijing, China (2014).
[29] Dong G., Wei J., Chen Z., Sun H., Yu X., Remaining Dischargeable Time Prediction for Lithium-Ion Batteries Using Unscented Kalman Filter, J. Power Sources, 364: 316-327 (2017).
[30] Kenney B., Darcovich K., MacNeil D.D., Davidson I.J., Modelling the Impact of Variations in Electrode Manufacturing on Lithium-Ion Battery Modules, J. Power Sources, 213: 391-401 (2012).
[31] Zhang C., Allafi W., Dinh Q., Ascencio P., Marco J., Online Estimation of Battery Equivalent Circuit Model Parameters and State of Charge Using Decoupled Least Squares Technique, Energy, 142: 678-688 (2018).