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Evaluation of the state of charge of lithium-ion batteries using ultrasonic guided waves and artificial neural network

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Abstract

In this paper, a method for detecting the state of charge (SOC) of lithium-ion (Li-ion) batteries based on ultrasonic guided waves and artificial neural network is proposed. Commercial Li-ion pouch batteries are taken as the experimental object, real-time ultrasonic guided wave detection is carried out during the operation of the battery, and the SOC is analyzed via signal processing. The guided wave parameters are taken as characteristic parameters, and the backpropagation (BP) neural network model is used to accurately estimate the battery SOC. It is found that the frequency band of the direct waves and the variation of their amplitude in the spectrum of the response signal have good correlations with the battery charge–discharge cycle. It is also found that the wave velocities of the two envelope peaks are the same as the change of the SOC, and the time of flight (TOF) decreases with the increase of the SOC. The research results can guide the development of a battery management system based on a guided wave framework that can be applied to the detection and monitoring of the SOC of Li-ion batteries.

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References

  1. Yang F, Wang D, Zhao Y, Tsui K-L, Bae SJ (2018) Energy 145:486–495

    Article  Google Scholar 

  2. Feng X, Ouyang M, Liu X, Lu L, Xia Y, He X (2018) Energy Storage Mater 10:246–267

    Article  Google Scholar 

  3. Lu L, Han X, Li J, Hua J, Ouyang M (2013) J Power Sources 226:272–288

    Article  CAS  Google Scholar 

  4. Balasingam, Ahmed M, Pattipati K (2020) Energies 13:2825

  5. Liu Z, Li Z, Zhang J, Su L, Ge H (2019) Energies 12:757

  6. Pop V, Bergveld HJ, Op het Veld JHG, Regtien PPL, Danilov D, Notten PHL (2006) J Electrochem Soc 153:A2013–A2022

    Article  CAS  Google Scholar 

  7. Moura SJ, Chaturvedi NA, Krstic M (2014) J Dyn Syst Meas Control Trans ASME 136:011015

  8. Lai X, Wang SY, Ma SD, Xie JY, Zheng YJ (2020) Electrochimica Acta 330:135239

  9. Wu J, Wang Y, Zhang X, Chen Z (2016) J Power Sources 327:457–464

    Article  CAS  Google Scholar 

  10. Kashkooli AG, Fathiannasab H, Mao Z, Chen Z (2019) J Electrochem Soc 166:A605–A615

    Article  CAS  Google Scholar 

  11. Nuhic A, Terzimehic T, Soczka-Guth T, Buchholz M, Dietmayer K (2013) J Power Sources 239:680–688

    Article  CAS  Google Scholar 

  12. Klass V, Behm M, Lindbergh G (2014) J Power Sources 270:262–272

    Article  CAS  Google Scholar 

  13. Fasahat M, Manthouri M (2020) J Power Sources 469:228375

  14. Aymerich F, Meili S (2000) Composites Part B-Engineering 31:1–6

    Article  Google Scholar 

  15. Nam K-W, Bak S-M, Hu E, Yu X, Zhou Y, Wang X, Wu L, Zhu Y, Chung K-Y, Yang X-Q (2013) Adv Func Mater 23:1047–1063

    Article  CAS  Google Scholar 

  16. Tsuda T, Imanishi A, Sano T, Sawamura A, Kamidaira T, Chen C-Y, Uchida S, Kusumoto S, Ishikawa M, Kuwabata S (2018) Electrochim Acta 279:136–142

    Article  CAS  Google Scholar 

  17. Sharma N, Peterson VK, Elcombe MM, Avdeev M, Studer AJ, Blagojevic N, Yusoff R, Kamarulzaman N (2010) J Power Sources 195:8258–8266

    Article  CAS  Google Scholar 

  18. Wang X, Zhang Q-L, Wang F-M (2012) Tribol Trans 55:747–751

    Article  CAS  Google Scholar 

  19. Troltzsch U, Kanoun O, Trankler HR (2006) Electrochim Acta 51:1664–1672

    Article  Google Scholar 

  20. Suarez-Hernandez R, Ramos-Sanchez G, Santos-Mendoza IO, Guzman-Gonzalez G, Gonzalez I (2020) J Electrochem Soc 167:100529

  21. Sood B, Osterman M, Pecht M (2013) IEEE Symp Prod Compliance Eng (ISPCE) 2013:1–6

    Google Scholar 

  22. Hsieh AG, Bhadra S, Hertzberg BJ, Gjeltema PJ, Goy A, Fleischer JW, Steingart DA (2015) Energy Environ Sci 8:1569–1577

    Article  CAS  Google Scholar 

  23. Gold L, Bach T, Virsik W, Schmitt A, Muller J, Staab TEM, Sextl G (2017) J Power Sources 343:536–544

    Article  CAS  Google Scholar 

  24. Davies G, Knehr KW, Van Tassell B, Hodson T, Biswas S, Hsieh AG, Steingart DA (2017) J Electrochem Soc 164:A2746–A2755

    Article  CAS  Google Scholar 

  25. Roy S, Ladpli P, Chang FK (2015) J Sound Vibr 351:206–220

    Article  Google Scholar 

  26. Ladpli P, Kopsaftopoulos F, Chang FK (2018) J Power Sources 384:342–354

    Article  CAS  Google Scholar 

  27. Popp H, Koller M, Keller S, Glanz G, Klambauer R, Bergmann A (2019) IEEE Access 7:170992–171000

    Article  Google Scholar 

  28. Zhao G, Liu Y, Liu G, Jiang S, Hao W (2021) J Energy Storage 39:102657

    Article  Google Scholar 

  29. Feldman M (2011) Mech Syst Signal Process 25:735–802

    Article  Google Scholar 

  30. Kato H, Kobayashi Y, Miyashiro H (2018) J Power Sources 398:49–54

    Article  CAS  Google Scholar 

  31. Tian Z, Yu L (2014) J Intell Mater Syst Struct 25:1107–1123

    Article  Google Scholar 

Download references

Funding

The authors are grateful for the financial support provided by the National Natural Science Foundation of China (Grant Nos. 11872025) and the Six Talent Peaks Project in Jiangsu Province (Grant No. 2019-KTHY-059).

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Authors and Affiliations

  1. College of Mechanical Engineering, Yangzhou University, Yangzhou, 225127, Jiangsu, China

    Yu Liu & Wenfeng Hao

  2. Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang, 212013, Jiangsu, China

    Yu Liu

  3. School of Mechanical Engineering and Automation, Wuhan Textile University, Wuhan, 430200, Hubei, China

    Renchao Zhang

Authors
  1. Yu Liu
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  2. Renchao Zhang
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  3. Wenfeng Hao
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Correspondence to Wenfeng Hao.

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Liu, Y., Zhang, R. & Hao, W. Evaluation of the state of charge of lithium-ion batteries using ultrasonic guided waves and artificial neural network. Ionics 28, 3277–3288 (2022). https://doi.org/10.1007/s11581-022-04568-6

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  • DOI: https://doi.org/10.1007/s11581-022-04568-6

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