Journal of Power Electronics

  • 제20권2호
  • /
  • Pages.614-623
  • /
  • 2020
  • /
  • 1598-2092(pISSN)
  • /
  • 2093-4718(eISSN)
전력전자학회 (The Korean Institute of Power Electronics)
권호 표지
DOI QR코드

DOI QR Code

New SOC estimation method under multi-temperature conditions based on parametric-estimation OCV

  • Wang, Qiuting (Department of Information and Electronic Engineering, Zhejiang University City College) ;
  • Qi, Wei (Department of Information and Electronic Engineering, Zhejiang University City College)
  • 투고 : 2019.06.08
  • 심사 : 2019.11.12
  • 발행 : 2020.03.20
⟨ 이전 논문 다음 논문 ⟩

초록

A new state of charge (SOC) estimation method under multi-temperature conditions is presented. The model parameters and SOC value of a lithium-ion battery cell are calculated based on the parametric-estimation open circuit voltage (OCV). The main efforts of this study are as follows. First, the OCV value is obtained based on an equivalent circuit model. Second, the model parameters and states are estimated using an adaptive joint extended Kalman filter. Third, the parametric OCV mapping model (OCVPE-SOC) is put forward to reduce systematic errors. Lastly, the online SOC value is estimated based on the battery model, the model parameters and the OCVPE-SOC mapping model. The noise covariance matrix is updated iteratively using an innovation sequence. The mapping model is established under a dynamic stress test cycle test and the verification results are obtained under the federal urban driving schedule cycle test. The obtained experimental results indicate that the proposed method can effectively reduce the system error and that a more accurate SOC estimation value can be obtained under different temperatures.

키워드

참고문헌

  1. Kim, J., Cho, B.H.: Pattern recognition for temperature-dependent state-of-charge/capacity estimation of a Li-ion cell. IEEE Trans. Energy Convers. 28(1), 1-11 (2013) https://doi.org/10.1109/TEC.2012.2222884
  2. Cho, H., Choi, W., Go, J., et al.: A study on time-dependent low temperature power performance of a lithium-ion battery. J. Power Sources 198, 273-280 (2012) https://doi.org/10.1016/j.jpowsour.2011.09.111
  3. Jiang, J., Zhang, C.: Fundamentals and Applications of Lithium-ion Batteries in Electric Drive Vehicles, pp. 4-5. Wiley, Singapore (2015)
  4. Shouliang, H.: Research on Modular Cascade Motor System for Electric Vehicle Driving. Harbin Institute of Technology, Harbin (2015)
  5. Farmann, A., Waag, W., Sauer, D.U.: Adaptive approach for on-board impedance parameters and voltage estimation of lithium-ion batteries in electric vehicles. J. Power Sources 299, 176-188 (2015) https://doi.org/10.1016/j.jpowsour.2015.08.087
  6. Farmann, A., Waag, W., Marongiu, A., et al.: Critical review of on-board capacity estimation techniques for lithium-ion batteries in electric and hybrid electric vehicles. J. Power Sources 281, 114-130 (2015) https://doi.org/10.1016/j.jpowsour.2015.01.129
  7. Fleischer, C., Waag, W., Heyn, H., et al.: On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models. J. Power Sources 260, 276-291 (2014) https://doi.org/10.1016/j.jpowsour.2014.01.129
  8. Fleischer, C., Waag, W., Heyn, H., et al.: On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. parameter and state estimation. J. Power Sources 262, 457-482 (2014) https://doi.org/10.1016/j.jpowsour.2014.03.046
  9. Jian, W., Li, T., Zhang, H., Lei, Y., Zhou, G.: Research on modeling and SOC estimation of lithium iron phosphate battery at low temperature. Energy Procedia 152, 556-561 (2018) https://doi.org/10.1016/j.egypro.2018.09.210
  10. Li, J., Klee Barillas, J., Guenther, C., et al.: Sequential monte carlo filter for state estimation of LiFePO4 batteries based on an online updated model. J. Sources 247, 156-162 (2014) https://doi.org/10.1016/j.jpowsour.2013.08.099
  11. Hu, M., Li, Y., Li, S., Fu, C., Qin, D., Li, Z.: Lithium-ion battery modeling and parameter identification based on fractional theory. Energy 165, 153-163 (2018)
  12. Yongyuan, Q., Hongyue, Z., Shuhua, S.: Kalman filter and principle of integrated navigation. Northwest University of Technology, Xi'an (2015)
  13. Fu, Y., Tippets, C.A., Donev, E.U., et al.: Structural colors: from natural to artificial systems. Wiley Interdiscip. Rev. Nanomed. Nanobiotechnol. 8(5), 758-775 (2016) https://doi.org/10.1002/wnan.1396
  14. Xing, Y., He, W., Pecht, M., et al.: State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperature. Appl. Energy 113, 106-115 (2014) https://doi.org/10.1016/j.apenergy.2013.07.008
  15. Zhang, R., Xia, B., Li, B., Cao, L., Lai, Y., Zheng, W., Wang, H., Wang, W.: State of the art of lithium-ion battery SOC estimation for electrical vehicles. Energies 11(7), 18-20 (2018)
  16. Xiong, B., Zhao, J., Wei, Z., Skyllas-Kazacos, M.: Extended Kalman filter method for state of charge estimation of vanadium redox flow battery using thermal-dependent electrical model. J. Power Sources 262, 50-61 (2014) https://doi.org/10.1016/j.jpowsour.2014.03.110
  17. Wang, Y., Zhang, C., Chen, Z., et al.: A novel active equalization method for lithium-ion batteries in electric vehicles. Appl. Energy 145, 36-42 (2015) https://doi.org/10.1016/j.apenergy.2015.01.127
  18. Zhao, Y., Stein, P., Bai, Y., Al-Siraj, M., Yang, Y., Bai-Xiang, X.: A review on modeling of electro-chemo-mechanics in lithium-ion batteries. J. Power Sources 413, 259-283 (2019) https://doi.org/10.1016/j.jpowsour.2018.12.011
  19. Zhang, W., Wang, L., Wang, L., Liao, C.: An improved adaptive estimator for state-of-charge estimation of lithium-ion batteries. J. Power Sources 402, 422-433 (2018) https://doi.org/10.1016/j.jpowsour.2018.09.016
  20. Xiong, R., Sun, F., Gong, X., et al.: A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles. Appl. Energy 113, 1421-1433 (2013) https://doi.org/10.1016/j.apenergy.2013.09.006
  21. Pei, L., Wang, T., Lu, R., et al.: Development of a voltage relaxation model for rapid open-circuit voltage prediction in lithium-ion batteries. J. Power Sources 253, 412-418 (2014) https://doi.org/10.1016/j.jpowsour.2013.12.083
  22. Khan, M.R., Mulder, G., Van Mierlo, J.: An online framework for state of charge determination of battery systems using combined system identification approach. J. Power Sources 246, 629-641 (2014) https://doi.org/10.1016/j.jpowsour.2013.07.092
  23. Wang, Y., Zhang, C., Chen, Z.: A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using p filter. J. Power Sources 279, 306-311 (2015) https://doi.org/10.1016/j.jpowsour.2015.01.005
  24. Plett, G.L.: Extended Kalman filtering for battery management systems of Li PB-based HEV battery packs. J. Power Sources 134(2), 262-276 (2004) https://doi.org/10.1016/j.jpowsour.2004.02.032

피인용 문헌

  1. Online full-parameter identification and SOC estimation of lithium-ion battery pack based on composite electrochemical - dual circuit polarization modeling vol.675, pp.1, 2020, https://doi.org/10.1088/1755-1315/675/1/012192
  2. On-line adaptive asynchronous parameter identification of lumped electrical characteristic model for vehicle lithium-ion battery considering multi-time scale effects vol.517, 2020, https://doi.org/10.1016/j.jpowsour.2021.230725