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State-of-charge estimation for lithium-ion battery based on PNGV model and particle filter algorithm

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Abstract

The accurate state-of-charge (SOC) estimation for lithium-ion battery (LIB) cells and packs plays an important role in fulfilling efficient battery management. However, some estimation errors of LIB states and SOC are often encountered when using the conventional battery model and filter algorithms. Thus, this paper explores a SOC estimation method of LIB based on an improved partnership for a new generation of vehicle (PNGV) model and particle filter (PF) algorithm. First, a second-order PNGV model is constructed, and the effect of different SOC interval changes on battery model parameters is considered. Then, the improved PNGV model-based PF algorithm is employed to realize the real-time estimation of battery SOC. Finally, the proposed SOC estimation method is validated under constant current and dynamic urban dynamometer driving schedule (UDDS) conditions. Results show that the improved PNGV model of LIB integration with the PF algorithm can considerably enhance the accuracy of SOC estimation. Compared to the conventional PNGV model-based PF algorithm, the mean absolute error and root mean squared error of SOC estimation errors are, respectively, reduced by approximately 32.7% and 32.5% under the 0.5C rate discharge and UDDS conditions.

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References

  1. Yang, B., Wang, J., Cao, P., Zhu, T., Shu, H., Chen, J., et al.: Classification, summarization and perspectives on state-of-charge estimation of lithium-ion batteries used in electric vehicles: a critical comprehensive survey. J. Energy Storage. 39, 102572 (2021)

    Article  Google Scholar 

  2. Fleischer, C., Waag, W., Heyn, H.M., Sauer, D.U.: On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models: Part 1. Requirements, critical review of methods and modeling. J. Power. Sources. 260, 276–291 (2014)

    Article  Google Scholar 

  3. Yang, Z., Patil, D., Fahimi, B.: Electrothermal modeling of lithium-ion batteries for electric vehicles. IEEE Trans. Veh. Technol. 68(1), 170–179 (2018)

    Article  Google Scholar 

  4. Ningrum, P., Windarko, N.A., Suhariningsih, S.: Estimation of state of charge (SoC) using modified coulomb counting method with open circuit compensation for battery management system (BMS). JAREE (2021). https://doi.org/10.12962/jaree.v5i1.150

    Article  Google Scholar 

  5. Tian, J., Xiong, R., Shen, W., Lu, J.: State-of-charge estimation of LiFePO4 batteries in electric vehicles: a deep-learning enabled approach. App. Energy. 291, 116812 (2021)

    Article  Google Scholar 

  6. Ren, X., Liu, S., Yu, X., Dong, X.: A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM. Energy 234, 121236 (2021)

    Article  Google Scholar 

  7. Xing, Y.J., He, W., Pecht, M., Tsui, K.L.: State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl. Energy 113, 106–115 (2014)

    Article  Google Scholar 

  8. Zhang, S., Zhang, X.: A novel non-experiment-based reconstruction method for the relationship between open-circuit-voltage and state-of-charge/state-of-energy of lithium-ion battery. Electrochim. Acta 403, 139637 (2022)

    Article  Google Scholar 

  9. Guo, Y., Yang, Z., Liu, K., Zhang, Y., Feng, W.: A compact and optimized neural network approach for battery state-of-charge estimation of energy storage system. Energy 219, 119529 (2021)

    Article  Google Scholar 

  10. Li, S., Ju, C., Li, J., Fang, R., Tao, Z., Li, B., Zhang, T.: State-of-charge estimation of lithium-ion batteries in the battery degradation process based on recurrent neural network. Energies 14(2), 306 (2021)

    Article  Google Scholar 

  11. Chandran, V., Kpatil, C., Karthick, A., Ganeshaperumal, D., Rahim, R., Ghosh, A.: State of charge estimation of lithium-ion battery for electric vehicles using machine learning algorithms. World Electr. Veh. J. 12(1), 38 (2021)

    Article  Google Scholar 

  12. Liu, X.T., Chen, Z.H., Zhang, C.B., Wu, J.: A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation. Appl. Energy 123, 263–272 (2014)

    Article  Google Scholar 

  13. Wang, Y.J., Zhang, C.B., Chen, Z.H.: A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries. Appl. Energy 135, 81–87 (2014)

    Article  Google Scholar 

  14. Wang, Y.J., Chen, Z.H.: A framework for state-of-charge and remaining discharge time prediction using unscented particle filter. Appl. Energy 260, 114324 (2020)

    Article  Google Scholar 

  15. Wei, Z.B., Meng, S.J., Xiong, B.Y., Ji, D.X., et al.: Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer. Appl. Energy 181, 332–341 (2016)

    Article  Google Scholar 

  16. Pang, H., Mou, L.J., Guo, L.: Parameter identification and state-of-charge estimation approach for enhanced lithium–ion battery equivalent circuit model considering influence of ambient temperatures. Chin. Phys. B 28(10), 566–574 (2019)

    Article  Google Scholar 

  17. Sun, D., Yu, X., Wang, C., Zhang, C., Huang, R., et al.: State of charge estimation for lithium-ion battery based on an intelligent adaptive extended kalman filter with improved noise estimator. Energy 214, 119025 (2021)

    Article  Google Scholar 

  18. Sturm, J., Ennifar, H., Erhard, S.V., Rheinfeld, A., Kosch, S., Jossen, A.: State estimation of lithium-ion cells using a physicochemical model based extended Kalman filter. Appl. Energy 223, 103–123 (2018)

    Article  Google Scholar 

  19. Peng, N., Zhang, S., Guo, X., Zhang, X.: Online parameters identification and state of charge estimation for lithium-ion batteries using improved adaptive dual unscented Kalman filter. Int. J. Energy Res. 45(1), 975–990 (2021)

    Article  Google Scholar 

  20. Meng, W.E.I., Jiabo, L.I., Zhongyu, L.I., Min, Y.E., Xinxin, X.U.: SOC estimation of Li-ion batteries based on Gaussian process regression and UKF. Energy Storage Sci. Technol. 9(4), 1206 (2020)

    Google Scholar 

  21. Fan, C.L.: Estimation of lithium battery SOC based on improved particle filter. Res. Exploration Lab. 37(01), 134–138 (2018)

    Google Scholar 

  22. Xia, B.Z., Sun, Z., Zhang, R.F., et al.: A comparative study of three improved algorithms based on particle filter algorithms in SOC estimation of lithium-ion batteries. Energies 10(8), 1149 (2017)

    Article  Google Scholar 

  23. Li, W.L., Liang, L.L.Y., Liu, W.J., Wu, X.H.: State of charge estimation of lithium-ion batteries using a discrete-time nonlinear observer. IEEE Trans. Industr. Electron.64(11), 8557–8565 (2017)

    Article  Google Scholar 

  24. Liu, Z., Wang, C., Guo, X., Cheng, S., Gao, Y., Wang, R., et al.: Thermal characteristics of ultrahigh power density lithium-ion battery. J. Power Sour. 506, 230205 (2021)

    Article  Google Scholar 

  25. Zhong, L., Zhang, C.B., He, Y., Chen, Z.H.: A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis. Appl. Energy 113, 558–564 (2014)

    Article  Google Scholar 

  26. Wei, Z.B., Hu, J., He, H.W., Li, Y., Xiong, B.Y.: Load current and state-of-charge co-estimation for current sensor-free lithium-ion battery. IEEE Trans. Power Electron. 36(10), 10970–10975 (2021)

    Article  Google Scholar 

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Acknowledgements

This work is supported by the Artificial Intelligence Technology Project of the Xi'an Science and Technology Bureau (No. 21RGZN0014). To the best of our knowledge, no conflict of interest, financial or others, exists. We have included acknowledgments, conflicts of interest, and funding sources after the discussion.

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Authors and Affiliations

  1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an, China

    Yuanfei Geng, Hui Pang & Xiaofei Liu

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  1. Yuanfei Geng
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  2. Hui Pang
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  3. Xiaofei Liu
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Correspondence to Hui Pang.

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Geng, Y., Pang, H. & Liu, X. State-of-charge estimation for lithium-ion battery based on PNGV model and particle filter algorithm. J. Power Electron. 22, 1154–1164 (2022). https://doi.org/10.1007/s43236-022-00422-0

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  • DOI: https://doi.org/10.1007/s43236-022-00422-0

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