Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
권호 표지
DOI QR코드

DOI QR Code

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

  • Geng, Yuanfei (School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology) ;
  • Pang, Hui (School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology) ;
  • Liu, Xiaofei (School of Mechanical and Precision Instrument Engineering, Xi'an University of Technology)
  • Received : 2022.01.03
  • Accepted : 2022.03.15
  • Published : 2022.07.20
⟨ Previous Next ⟩

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.

Keywords

Acknowledgement

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.

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) https://doi.org/10.1016/j.est.2021.102572
  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) https://doi.org/10.1016/j.jpowsour.2014.01.129
  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) https://doi.org/10.1109/TVT.2018.2880138
  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
  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) https://doi.org/10.1016/j.apenergy.2021.116812
  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) https://doi.org/10.1016/j.energy.2021.121236
  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) https://doi.org/10.1016/j.apenergy.2013.07.008
  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) https://doi.org/10.1016/j.electacta.2021.139637
  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) https://doi.org/10.1016/j.energy.2020.119529
  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) https://doi.org/10.3390/en14020306
  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) https://doi.org/10.3390/wevj12010038
  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) https://doi.org/10.1016/j.apenergy.2014.02.072
  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) https://doi.org/10.1016/j.apenergy.2014.08.081
  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) https://doi.org/10.1016/j.apenergy.2019.114324
  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) https://doi.org/10.1016/j.apenergy.2016.08.103
  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)
  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) https://doi.org/10.1016/j.energy.2020.119025
  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) https://doi.org/10.1016/j.apenergy.2018.04.011
  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) https://doi.org/10.1002/er.6088
  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)
  21. Fan, C.L.: Estimation of lithium battery SOC based on improved particle filter. Res. Exploration Lab. 37(01), 134-138 (2018)
  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) https://doi.org/10.3390/en10081149
  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) https://doi.org/10.1109/TIE.2017.2703685
  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) https://doi.org/10.1016/j.jpowsour.2021.230205
  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) https://doi.org/10.1016/j.apenergy.2013.08.008
  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) https://doi.org/10.1109/TPEL.2021.3068725