Abstract
The accurate estimation of the state of charge (SOC) under the nonlinear model of all-vanadium redox flow battery (VRB) is studied in this paper. Based on the VRB equivalent circuit model, the recursive least squares (RLS) algorithm is used to identify the model parameters and verify the correctness of the model in the constant current charging process. Then, unsupervised Kalman filter (UKF) algorithm is used to estimate SOC and compared with the extended Kalman filter (EKF) estimation results. Simulation experiments show that the UKF algorithm can accurately estimate the SOC faster, with an error less than 2%. In addition, analyzing the influence of initial value of SOC verifies the convergence and anti-interference ability of the algorithm.
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Acknowledgements
This work is supported by National Key R&D Program of China (No. 2017YFB1201003-006).
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Sun, G., Hao, Y., Li, Z., Wang, L., Fang, K. (2020). SOC Estimation of All-Vanadium Redox Flow Battery via Parameters Identification and UKF Algorithm. In: Jia, L., Qin, Y., Liu, B., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 4th International Conference on Electrical and Information Technologies for Rail Transportation (EITRT) 2019. EITRT 2019. Lecture Notes in Electrical Engineering, vol 638. Springer, Singapore. https://doi.org/10.1007/978-981-15-2862-0_84
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DOI: https://doi.org/10.1007/978-981-15-2862-0_84
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