A reliable estimation method for mining lithium-ion battery
Pages 25 - 39
Abstract
Power battery SOC (state of charge, SOC) is one of the important decision-making factors of energy management. Accurate estimation plays an important role in optimizing vehicle energy management and improving the utilization of power battery energy. The key to accurate estimation of SOC is to determine circuit model parameters and estimation methods. The research object of this article is lithium manganese oxide battery for mining (LiMn2O4). The experiments of multiplying power, temperature and HPPC (hybrid pulse power characteristic, HPPC) are carried out. A self-tuning calculation method of dynamic system is proposed, and the dynamic self-tuning model based on second-order RC is established. At the same time, in view of the shortcoming that the UKF (Unscented Kalman Filter, UKF) algorithm cannot estimate the noise in real time, In order to improve the accuracy of battery SOC estimation, an adaptive square root unscented Kalman filter (ASR-UKF) algorithm is proposed, which can make the noise statistical characteristics follow the estimation results for adaptive adjustment. Finally, the constant current and dynamic conditions are tested. The results show that the maximum change rate of model parameters with magnification is 76%, and the maximum change rate with temperature is 73.7%. The analysis of dynamic characteristics is a key factor to improve the accuracy of SOC estimation; ASR-UKF Compared with the UKF algorithm, the error is reduced by 78% under constant current conditions and 85.7% under dynamic conditions. The reliability and real-time performance of the algorithm can be obtained by comparing the simulation data with the actual data. The conclusions of this paper can be used as a theoretical basis, which can be used for model analysis of lithium batteries for mining and estimation of internal state variables.
References
[1]
G.Z. Dong, J.W. Wei and Z.H. Chen, Remaining dischargeable time prediction for lithium-ion batteries using unscented Kalman filter, Journal of Power Sources 36(4) (2017), 316–327.
[2]
J. Xu, Y. Dou and Z. Wei, Recent progress in graphite intercalation compounds for rechargeable metal (Li, Na, K, Al)-ion batteries, Advanced Science 4(10) (2017), 146–170.
[3]
C.Y. Tang, K. Leung and R.T. Haasch, LiMn2O4 surface chemistry evolution during cycling revealed by in situ auger electron spectroscopy and X-ray photoelectron spectroscopy, ACS Applied Materials & Interfaces 9(39) (2017), 968–978.
[4]
Y.H. Zhang, W.J. Song and S.L. Lin, A novel model of the initial state of charge estimation for LiFePO4 batteries, Journal of Power Sources 24(8) (2014), 1028–1033.
[5]
H. Zhang, H.W. Mu and Y. Zhang, Calculation and characteristics analysis of lithium ion batteries internal resistance using HPPC test, Advanced Materials Research 9(26) (2014), 915–918.
[6]
F. Feng, R. Lu and C. Zhu, A combined state of charge estimation method for lithium-ion batteries used in a wide ambient temperature range, Energies 7(5) (2014), 3004–3032.
[7]
M. Wohlfahrt-Mehrens and C. Vogler, Aging mechanisms of lithium cathode materials, Journal of Power Sources 127(1) (2004), 58–64.
[8]
M. Luo, Y. Guo and J. Kang, Ternary-material lithium-ion battery SOC estimation under various ambient temperature, Ionics 24(7) (2018), 1907–1917.
[9]
X.S. Hu, F.C. Sun and Y. Zou, Comparison between two model-based algorithms for Li-ion battery SOC estimation in electric vehicles, Simulation Modelling Practice and Theory 34(12) (2013), 1–11.
[10]
R. Zhang, B. Xia and B. Li, State of the art of lithium-ion battery SOC estimation for electrical vehicles, Energies 11(7) (2018), 18–20.
[11]
Y.J. Xing, W. He and M. Pecht, State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures, Applied Energy 11(3) (2014), 106–115.
[12]
A.Q. Ansari, M. Hasan and N. Islam, Novel optimization technique to charge e-rickshaw battery using single sensor based MPPT of SPV module, Journal of Intelligent & Fuzzy Systems 35(5SI) (2018), 5077–5084.
[13]
X. Wang and G. Jiao, Research on association rules of course grades based on parallel FP-growth algorithm, Journal of Computational Methods in Sciences and Engineering 20(3) (2020), 759–769.
[14]
X. Zhang, W. Zhang and H. Li, Review on state of charge estimation methods for li-ion batteries, Transactions on Electrical and Electronic Materials 18(3) (2017), 136–140.
[15]
X.S. Hu, S. Li and H. Peng, A comparative study of equivalent circuit models for Li-ion batteries, Journal of Power Sources 198(1) (2012), 359–367.
[16]
A.M. Bizeray, S. Zhao and S.R. Duncan, lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter, Journal of Power Sources 29(6) (2015), 400–412.
[17]
L. Zhang, L. Zhang and C. Papavassiliou, Intelligent computing for extended Kalman filtering SOC algorithm of lithium-ion battery, Wireless Personal Communications 25(4) (2018), 1–14.
[18]
S. Li, M. Hu and Y. Li, Fractional-order modeling and SOC estimation of lithium-ion battery considering capacity loss, International Journal of Energy Research 43(1) (2019), 417–429.
[19]
G. Lei, X. Luo, L. Cai, L. Gao, N. Dai, Q. Xu and Z. Tan, Research on smart EFK algorithm for electric vehicle battery packs management system, Journal of Intelligent & Fuzzy Systems 38(1) (2020), 257–262.
[20]
H. Chaoui, C. Ibe-Ekeocha, A. El Mejdoubi, A. Oukaour, H. Gualous and N. Omar, Aging prediction and state of charge estimation of a LiFePO4, battery using input time-delayed neural networks, Electric Power Systems Research 14(6) (2017), 189–197.
[21]
J. Chen, Q. Ouyang and C. Xu, Neural Network-based state of charge observer design for lithium-ion batteries, IEEE Transactions on Control Systems Technology 14(9) (2017), 1–9.
[22]
C. Zhang, K. Li and L. Pei, An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries, Journal of Power Sources 28(3) (2015), 24–36.
[23]
S.S. Gao, G.G. Hu and Y.M. Zhong, Windowing and random weighting based adaptive unscented Kalman filter, International Journal of Adaptive Control and Signal Processing 29(2) (2015), 201–223.
[24]
J.Y. Liu, H.X. Wang and Y.Y. Sun, Real-coded quantum-inspired genetic algorithm-based BP neural network algorithm, Mathematical Problems in Engineering 56(32) (2015), 1–10.
[25]
H. Liu, H. Ting and X. Liang, New wind speed forecasting approaches using fast ensemble empirical model decomposition, genetic algorithm, mind evolutionary algorithm and artificial neural networks, Renewable Energy 83(25) (2015), 1066–1075.
[26]
Y. Ma, J. Ru and N. Yi, Electtrochemical modeling parameter identification based on bacterial foraging optimization algorithm for lithium-ion batteries, Journal of Applied Electrochemistry 46(11) (2016), 1–13.
[27]
S. Suriyakumar, M. Kathiresan and A.M. Stephan, Charge-discharge and interfacial properties of ionic liquid-added hybrid electrolytes for lithium-sulfur batteries, ACS Omega 4(2) (2019), 3894–3903.
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Published: 01 January 2022
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