Journal of Power Electronics

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

DOI QR Code

Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short- and longer term operating conditions

  • Ren, Pu (School of Information Engineering, Southwest University of Science and Technology) ;
  • Wang, Shunli (School of Information Engineering, Southwest University of Science and Technology) ;
  • He, Mingfang (School of Information Engineering, Southwest University of Science and Technology) ;
  • Cao, Wen (School of Information Engineering, Southwest University of Science and Technology)
  • Received : 2021.03.02
  • Accepted : 2021.04.15
  • Published : 2021.08.20
⟨ Previous Next ⟩

Abstract

To solve the problems in estimating the state of health (SOH) of Li-ion batteries due to real-time estimation difficulty and low precision under various operating conditions, the variations of the SOH caused by increases of the internal resistance have been analyzed. Based on the second-order RC equivalent circuit model, the short-term effect of the state of charge (SOC) on the internal resistance was considered, which was set under the discharge condition. In addition, the variation of the internal resistance was analyzed in two intervals of 0-1 s and 1-10 s. The extended Kalman filter (EKF) algorithm was improved to present a novel improved Kalman filter (IKF) algorithm to accurately predict the long-term internal resistance under different operating conditions. A computational formula based on the internal-resistance increasing was established and the SOH was estimated. The error of the calculated result when compared with the forgetting factor least square method based on the internal-resistance increasing was controlled to within 4.0% under the HPPC condition, 3.0% under the BBDST condition, and 6.0% under the DST condition. The proposed algorithm has good convergence, helps improve the SOH estimation, and encourages the application of Li-ion batteries.

Keywords

Acknowledgement

The work was supported by National Natural Science Foundation of China (No. 61801407), Sichuan science and technology program (No. 20019YFG0427), China Scholarship Council (No. 201908515099) and Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province (No. 18kftk03), Natural Science Foundation of and Southwest University of Science and Technology (Nos. 17zx7110, 18zx7145).

References

  1. Kawahara, Y., et al.: Development of status detection method of lithium-ion rechargeable battery for hybrid electric vehicles. J. Power Sources 481, 228760 (2021) https://doi.org/10.1016/j.jpowsour.2020.228760
  2. Cai, L., et al.: Multiobjective optimization of data-driven model for lithium-ion battery SOH estimation with short-term feature. IEEE Trans. Power Electron. 35(11), 11855-11864 (2020) https://doi.org/10.1109/tpel.2020.2987383
  3. Dai, H., et al.: A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain. IEEE Trans. Ind. Electron. 66(10), 7706-7716 (2019) https://doi.org/10.1109/tie.2018.2880703
  4. Wang, Y., Chen, Z.: A framework for state-of-charge and remaining discharge time prediction using unscented p filter. Appl. Energy 260, 114324 (2020) https://doi.org/10.1016/j.apenergy.2019.114324
  5. Wei, Z., et al.: Future smart battery and management: advanced sensing from external to embedded multi-dimensional measurement. J. Power Sources 489, 229462 (2021) https://doi.org/10.1016/j.jpowsour.2021.229462
  6. Wei, Z., et al.: Noise-immune model identification and state-of-charge estimation for lithium-ion battery using bilinear parameterization. IEEE Trans. Ind. Electron. 68(1), 312-323 (2021) https://doi.org/10.1109/tie.2019.2962429
  7. Feng, X., et al.: Online state-of-health estimation for Li-ion battery using partial charging segment based on support vector machine. IEEE Trans. Veh. Technol. 68(9), 8583-8592 (2019) https://doi.org/10.1109/tvt.2019.2927120
  8. Tian, J., Xiong, R., Shen, W.: State-of-health estimation based on differential temperature for lithium ion batteries. IEEE Trans. Power Electron. 35(10), 10363-10373 (2020) https://doi.org/10.1109/tpel.2020.2978493
  9. Gou, B., Xu, Y., Feng, X.: State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method. IEEE Trans. Veh. Technol. 69(10), 10854-10867 (2020) https://doi.org/10.1109/tvt.2020.3014932
  10. Li, P., et al.: State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. J. Power Sources 459, 228069 (2020) https://doi.org/10.1016/j.jpowsour.2020.228069
  11. Hu, X., et al.: State estimation for advanced battery management: key challenges and future trends. Renew. Sustain. Energy Rev. 114, 109334 (2019) https://doi.org/10.1016/j.rser.2019.109334
  12. Feng, F., et al.: Co-estimation of lithium-ion battery state of charge and state of temperature based on a hybrid electrochemical-thermal-neural-network model. J. Power Sources 455, 227935 (2020) https://doi.org/10.1016/j.jpowsour.2020.227935
  13. Wang, Y., Li, M., Chen, Z.: Experimental study of fractional-order models for lithium-ion battery and ultra-capacitor: modeling, system identification, and validation. Appl. Energy 278, 115736 (2020) https://doi.org/10.1016/j.apenergy.2020.115736
  14. Bian, X., et al.: An open circuit voltage-based model for state-of-health estimation of lithium-ion batteries: model development and validation. J. Power Sources 448, 227401 (2020) https://doi.org/10.1016/j.jpowsour.2019.227401
  15. Li, S., He, H., Li, J.: Big data driven lithium-ion battery modeling method based on SDAE-ELM algorithm and data pre-processing technology. Appl. Energy 242, 1259-1273 (2019) https://doi.org/10.1016/j.apenergy.2019.03.154
  16. Tang, X., et al.: Real-time aging trajectory prediction using a base model-oriented gradient-correction p filter for Lithium-ion batteries. J. Power Sources 440, 227118 (2019) https://doi.org/10.1016/j.jpowsour.2019.227118
  17. Tang, X., et al.: Model migration based battery power capability evaluation considering uncertainties of temperature and aging. J. Power Sources 440, 227141 (2019) https://doi.org/10.1016/j.jpowsour.2019.227141
  18. Wang, Y., et al.: A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems. Renew. Sustain. Energy Rev. 131, 110015 (2020) https://doi.org/10.1016/j.rser.2020.110015
  19. Zhang, Y., et al.: State of charge-dependent aging mechanisms in graphite/Li(NiCoAl)O-2 cells: capacity loss modeling and remaining useful life prediction. Appl. Energy 255, 113818 (2019) https://doi.org/10.1016/j.apenergy.2019.113818
  20. Bian, X., Liu, L., Yan, J.: A model for state-of-health estimation of lithium ion batteries based on charging profiles. Energy 177, 57-65 (2019) https://doi.org/10.1016/j.energy.2019.04.070
  21. Xu, M., et al.: Fast charging optimization for lithium-ion batteries based on dynamic programming algorithm and electrochemical-thermal-capacity fade coupled model. J. Power Sources 438, 227015 (2019) https://doi.org/10.1016/j.jpowsour.2019.227015
  22. Pan, W., et al.: A data-driven fuzzy information granulation approach for battery state of health forecasting. J. Power Sources 475, 228716 (2020) https://doi.org/10.1016/j.jpowsour.2020.228716
  23. Wang, Y., et al.: A fractional-order model-based state estimation approach for lithium-ion battery and ultra-capacitor hybrid power source system considering load trajectory. J. Power Sources 449, 227543 (2020) https://doi.org/10.1016/j.jpowsour.2019.227543
  24. Zhang, S., et al.: A rapid online calculation method for state of health of lithium-ion battery based on coulomb counting method and differential voltage analysis. J. Power Sources 479, 228740 (2020) https://doi.org/10.1016/j.jpowsour.2020.228740
  25. Tan, Y., Zhao, G.: Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries. IEEE Trans. Ind. Electron. 67(10), 8723-8731 (2020) https://doi.org/10.1109/tie.2019.2946551
  26. Song, Y., et al.: A hybrid statistical data-driven method for on-line joint state estimation of lithium-ion batteries. Appl. Energy 261, 114408 (2020) https://doi.org/10.1016/j.apenergy.2019.114408
  27. Naseri, F., et al.: Online parameter estimation for supercapacitor state-of-energy and state-of-health determination in vehicular applications. IEEE Trans. Ind. Electron. 67(9), 7963-7972 (2020) https://doi.org/10.1109/tie.2019.2941151
  28. Sarmah, S.B., et al.: Numerical and experimental investigation of state of health of Li-ion battery. Int. J. Green Energy 17(8), 510-520 (2020) https://doi.org/10.1080/15435075.2020.1763360
  29. Ma, Z., et al.: Multilayer SOH equalization scheme for MMC battery energy storage system. IEEE Trans. Power Electron. 35(12), 13514-13527 (2020) https://doi.org/10.1109/tpel.2020.2991879
  30. Hu, X., et al.: Battery lifetime prognostics. Joule 4(2), 310-346 (2020) https://doi.org/10.1016/j.joule.2019.11.018
  31. Hu, X., et al.: An enhanced multi-state estimation hierarchy for advanced lithium-ion battery management. Appl. Energy 257, 114019 (2020) https://doi.org/10.1016/j.apenergy.2019.114019
  32. Xiao, D., et al.: Reduced-coupling coestimation of SOC and SOH for lithium-ion batteries based on convex optimization. IEEE Trans. Power Electron. 35(11), 12332-12346 (2020) https://doi.org/10.1109/tpel.2020.2984248
  33. Liu, K., et al.: Lithium-ion battery charging management considering economic costs of electrical energy loss and battery degradation. Energy Convers. Manag. 195, 167-179 (2019) https://doi.org/10.1016/j.enconman.2019.04.065
  34. Li, W., et al.: Parameter sensitivity analysis of electrochemical model-based battery management systems for lithium-ion batteries. Appl. Energy 269, 115104 (2020) https://doi.org/10.1016/j.apenergy.2020.115104
  35. Li, J., Landers, R.G., Park, J.: A comprehensive single-p-degradation model for battery state-of-health prediction. J. Power Sources 456, 227950 (2020) https://doi.org/10.1016/j.jpowsour.2020.227950