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Estimation of fractional SOC for lithium batteries based on OCV hysteretic characteristics

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Abstract

Lithium battery state of charge (SOC) estimation is an important part of the battery management system and is of great significance to the safe and efficient operation of the battery. This paper first analyzes the hysteresis characteristics of battery charging and discharging through the hysteresis main loop and small loop characteristic tests, and constructs a hysteresis model that can correct the hysteresis voltage. Then, the principle of fractional-order calculus was introduced into the traditional integer-order model, and a constant phase element (CPE) was used to describe the fractional-order dynamic characteristics of the battery. Combined with the hysteretic model, a fractional-order hysteretic equivalent circuit model was constructed., and use genetic algorithm to identify the model parameters. Improvements are proposed to address the estimation bias and filter divergence of the extended Kalman filter algorithm. Correlation coefficients and adaptive factors are added to adaptively update the noise and Kalman gains to estimate battery SOC. Finally, the DST working condition experiment shows that the SOC error of the method proposed in this article is about 1.53%, the calculation time is 0.6 s, and the absolute correlation coefficient is 0.9953.

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References

  1. Ali MU, Zafar A, Nengroo SH, Hussain S, Junaid Alvi M, Kim H-J (2019) Towards a smarter battery management system for electric vehicle applications: a critical review of lithium-ion battery state of charge estimation. Energies 12(3):446

    Article  CAS  Google Scholar 

  2. Mawonou KS, Eddahech A, Dumur D, Beauvois D, Godoy E (2019) Improved state of charge estimation for li-ion batteries using fractional order extended Kalman filter. J Power Sources 435

  3. Zhu Q, Xu M, Liu W, Zheng M (2019) A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended Kalman filter. Energy 187

  4. Liu C, Hu M, Jin G, Xu Y, Zhai J (2021) State of power estimation of lithium-ion battery based on fractional-order equivalent circuit model. J Energy Storage 41:102954

    Article  Google Scholar 

  5. Lee Y-T, Kuo C-T, Yew T-R (2020) Investigation on the voltage hysteresis of mn3o4 for lithium-ion battery applications. ACS Appl Mater Interfaces 13(1):570–579

    Article  PubMed  Google Scholar 

  6. Li R, Li Y, Zhang R, He M, Ma Y, Huo H, Zuo P, Yin G (2021) Voltage hysteresis of magnesium anode: taking magnesium-sulfur battery as an example. Electrochimica Acta 369

  7. Jiang Y, Offer G, Jiang J, Marinescu M, Wang H (2020) Voltage hysteresis model for silicon electrodes for lithium ion batteries, including multi-step phase transformations, crystallization and amorphization. J Electrochemical Soc 167(13):130533

    Article  CAS  Google Scholar 

  8. Yamamoto M, Terauchi Y, Sakuda A, Kato A, Takahashi M (2020) Effects of volume variations under different compressive pressures on the performance and microstructure of all-solid-state batteries. J Power Sources 473:228595

    Article  CAS  Google Scholar 

  9. Xu Z, Wang J, Fan Q, Lund PD, Hong J (2020) Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique. J Energy Storage 32:101678

    Article  Google Scholar 

  10. Shi H, Wang S, Fernandez C, Huang J, Xu W, Wang L (2022) Battery hysteresis compensation modeling and state-of-charge estimation adaptive to time-varying ambient temperature conditions. Int J Energy Res 46(12):17096–17112

    Article  Google Scholar 

  11. Choi E, Chang S (2020) A temperature-dependent state of charge estimation method including hysteresis for lithium-ion batteries in hybrid electric vehicles. Ieee Access 8:129857–129868

    Article  Google Scholar 

  12. Zhu G, Wu O, Wang Q, Kang J, Wang JV (2023) The modeling and SOC estimation of a LiFePO4 battery considering the relaxation and overshoot of polarization voltage. Batteries 9(7):369

    Article  CAS  Google Scholar 

  13. Meng J, Boukhnifer M, Diallo D, Wang T (2020) Short-circuit fault diagnosis and state estimation for li-ion battery using weighting function self-regulating observer. In: 2020 Prognostics and health management conference (PHM-Besançon), IEEE, pp 15–20

  14. Almagbile A, Wang J, Ding W (2010) Evaluating the performances of adaptive Kalman filter methods in GPS/INS integration. J Glob Position Syst 9(1):33–40

    Article  Google Scholar 

  15. Barai A, Widanage WD, Marco J, McGordon A, Jennings P (2015) A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells. J Power Sources 295:99–107

    Article  CAS  Google Scholar 

  16. He Y, He R, Guo B, Zhang Z, Yang S, Liu X, Zhao X, Pan Y, Yan X, Li S (2020) Modeling of dynamic hysteresis characters for the lithium-ion battery. J Electrochemical Soc 167(9):090532

    Article  CAS  Google Scholar 

  17. Antonucci V, Artale G, Brunaccini G, Caravello G, Cataliotti A, Cosentino V, Di Cara D, Ferraro M, Guaiana S, Panzavecchia N et al (2019) Li-ion battery modeling and state of charge estimation method including the hysteresis effect. Electronics 8(11):1324

    Article  CAS  Google Scholar 

  18. Wang B, Li SE, Peng H, Liu Z (2015) Fractional-order modeling and parameter identification for lithium-ion batteries. J Power Sources 293:151–161

  19. Ferahtia S, Djeroui A, Rezk H, Chouder A, Houari A, Machmoum M (2021) Optimal parameter identification strategy applied to lithium-ion battery model. Int J Energy Res 45(11):16741–16753

  20. Han J-X, Ma M-Y, Wang K (2021) Product modeling design based on genetic algorithm and BP neural network. Neural Comput Appl 33:4111–4117

    Article  Google Scholar 

  21. Katoch S, Chauhan SS, Kumar V (2021) A review on genetic algorithm: past, present, and future. Multimed Tools Appl 80:8091–8126

    Article  PubMed  Google Scholar 

  22. Qiao S, Fan Y, Wang G, Mu D, He Z (2022) Radar target tracking for unmanned surface vehicle based on square root Sage-Husa adaptive robust Kalman filter. Sensors 22(8):2924

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yang Y, Xu T (2003) An adaptive Kalman filter based on sage windowing weights and variance components. J Navigation 56(2):231–240

    Article  Google Scholar 

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Funding

Jiangsu University of Technology Graduate Student Fund

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Contributions

H.Z. (first author): conceptualization, methodology, software, investigation, formal analysis, writing—original draft; F.L.: data curation, writing—original draft, software, validation; H.H.: visualization, investigation; X.S.: resources, supervision; author H.Z. (corresponding author): conceptualization, funding acquisition, resources, supervision, writing—review and editing.

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Correspondence to Haizhong Chen.

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Chen, H., Liu, F., Hou, H. et al. Estimation of fractional SOC for lithium batteries based on OCV hysteretic characteristics. Ionics 30, 2627–2641 (2024). https://doi.org/10.1007/s11581-024-05442-3

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