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Modeling Theory of Lithium-Ion Batteries

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Abstract

The complex electrochemical reactions inside the batteries are affected by many influencing factors and uncertainties. Establishing mathematical models of batteries is seen as a multidisciplinary problem, for which it has always been an important yet difficult problem in academia and industry.

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References

  1. Fuller TF, Doyle M, Newman J (1994) Simulation and optimization of the dual Lithium ion insertion cell. J Electrochem Soc 141:1–10

    Article  Google Scholar 

  2. Lin C, Tang A (2016) Simplification and efficient simulation of electrochemical model for Li-ion battery in EVs. Energy Procedia 104:68–73

    Article  Google Scholar 

  3. Lin C, Tang A, Xing J (2017) Evaluation of electrochemical models based battery state-of-charge estimation approaches for electric vehicles. Appl Energy 207:394–404

    Article  Google Scholar 

  4. Cai L, White RE (2009) Reduction of model order based on proper orthogonal decomposition for Lithium-Ion battery simulations. J Electrochem Soc 156(3):154–161

    Article  Google Scholar 

  5. Xiong R, Li L, Tian J (2018) Towards a smarter battery management system: a critical review on battery state of health monitoring methods. J Power Sources 405:18–29

    Article  Google Scholar 

  6. Xiong R, Li L, Li Z, Yu Q, Mu H (2018) An electrochemical model based degradation state identification method of lithium-ion battery for all-climate electric vehicles application. Appl Energy 219:264–275

    Article  Google Scholar 

  7. Ye M, Gong H, Xiong R, Mu H (2018) Research on the battery charging strategy with charging and temperature rising control awareness. IEEE Access 6:64193–64201

    Article  Google Scholar 

  8. Nejad S, Gladwin DT, Stone DA (2016) A systematic review of lumped-parameter equivalent circuit models for real-time estimation of lithium-ion battery states. J Power Sources 316:183–196

    Article  Google Scholar 

  9. Lin C, Mu H, Xiong R, Cao J (2017) Multi-model probabilities based state fusion estimation method of lithium-ion battery for electric vehicles: state-of-energy. Appl Energy 194:560–568

    Article  Google Scholar 

  10. Zhang Y, Xiong R, He H, Shen W (2017) Lithium-Ion battery pack state of charge and state of energy estimation algorithms using a hardware-in-the-loop validation. IEEE Trans Power Electron 32:4421–4431

    Article  Google Scholar 

  11. Yu Q, Xiong R, Lin C, Shen W, Deng J (2017) Lithium-ion battery parameters and state-of-charge joint estimation based on H-infinity and unscented Kalman filters. IEEE Trans Veh Technol 66:8693–8701

    Article  Google Scholar 

  12. Xiong R, Yu Q, Shen W, Lin C, Sun F (2019) A sensor fault diagnosis method for a lithium-ion battery pack in electric vehicles. IEEE Trans Power Electron 34(10):9709–9718

    Article  Google Scholar 

  13. Xiong R, Yu Q, Wang LY, Lin C (2017) A novel method to obtain the open circuit voltage for the state of charge of lithium ion batteries in electric vehicles by using H-infinity filter. Appl Energy 207:346–353

    Article  Google Scholar 

  14. Zhao X, Cai Y, Yang L, Deng Z, Qiang J (2017) State of charge estimation based on a new dual-polarization-resistance model for electric vehicles. Energy 135:40–52

    Article  Google Scholar 

  15. Zhang DH, Zhu GR, Bao J, Ma Y, He SJ, Qiu S, Chen W (2015) Research on parameter identification of battery model based on adaptive particle swarm optimization algorithm. J Comput Theor Nanosci 12:1362–1367

    Article  Google Scholar 

  16. Remmlinger J, Buchholz M, Soczka-Guth T, Dietmayer K (2013) On-board state-of-health monitoring of lithium-ion batteries using linear parameter-varying models. J Power Sources 239:689–695

    Article  Google Scholar 

  17. Deng Z, Yang L, Cai Y, Deng H, Sun L (2016) Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery. Energy 112:469–480

    Article  Google Scholar 

  18. Wei Z, Zou C, Leng F, Soong BH, Tseng K-J (2018) Online model identification and state-of-charge estimate for Lithium-ion battery with a recursive total least squares-based observer. IEEE Trans Ind Electron 65:1336–1346

    Article  Google Scholar 

  19. Sun F, Xiong R, He H (2016) A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique. Appl Energy 162:1399–1409

    Article  Google Scholar 

  20. Huang J, Li Z, Yann B, Zhang J (2016) Graphical analysis of electrochemical impedance spectroscopy data in Bode and Nyquist representations. J Power Sources 309:82–98

    Article  Google Scholar 

  21. Wang B, Liu Z, Li SE, Moura SJ, Peng H (2017) State-of-charge estimation for Lithium-ion batteries based on a nonlinear fractional model. IEEE Trans Control Syst Technol 25:3–11

    Article  Google Scholar 

  22. Yang R, Xiong R, He H, Chen Z (2018) A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application. J Clean Prod 187:950–959

    Article  Google Scholar 

  23. Ivo P (2010) Fractional-order nonlinear systems (modeling, analysis and simulation), 2nd ed. Springer

    Google Scholar 

  24. Xiong R, Tian J, Shen W, Sun F (2018) A novel fractional order model for state of charge estimation in lithium ion batteries. IEEE Trans Veh Technol 68(5):4130–4139

    Article  Google Scholar 

  25. Tian J, Xiong R, Yu Q (2019) Fractional-order model-based incremental capacity analysis for degradation state recognition of Lithium-ion batteries. IEEE Trans Ind Electron 66:1576–1584

    Article  Google Scholar 

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Correspondence to Rui Xiong .

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Xiong, R. (2020). Modeling Theory of Lithium-Ion Batteries. In: Battery Management Algorithm for Electric Vehicles . Springer, Singapore. https://doi.org/10.1007/978-981-15-0248-4_3

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  • DOI: https://doi.org/10.1007/978-981-15-0248-4_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0247-7

  • Online ISBN: 978-981-15-0248-4

  • eBook Packages: EnergyEnergy (R0)

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