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State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method

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Abstract

The effects of ambient temperature and the flat form characteristics of the open circuit voltage state-of-charge (SOC) curve for lithium iron phosphate batteries are the major issues that influence the accuracy of the SOC estimation, which is critical for estimating the driving range of electric vehicles, and the optimal charge control of batteries to prevent the sudden loss of power in battery-powered systems. We proposed a SOC estimation method by using a long short-term memory (LSTM)–recurrent neural network (RNN) to reduce the SOC estimation errors, and to develop a model for the sophisticated battery behaviors under varying ambient temperatures, including time-variable current, voltage, and temperature conditions. The proposed method was evaluated using data from the LiFePO4 battery obtained by the dynamic stress test. The experimental results show that the proposed method can accurately learn the influence of ambient temperatures on the battery and also estimate the battery's SOC under varying temperatures with root mean square errors less than 1.5% and mean average errors less than 1%. Moreover, the proposed method also provides a sufficient SOC estimation under other temperature conditions. The main contribution of this study is the comprehensive explanation and implementation process of the data-based DL approach for the SOC estimation of the LIBs in the following aspects, (1) An LSTM-RNN was trained to model the complex battery dynamics under varying ambient temperatures. (2) The proposed method is model-free and data-driven approach, which means there is no need to construct OCV-SOC lookup tables under varying temperatures in order to pick an appropriate equivalent circuit model. The proposed method can be extended for the SOC estimation of other types of lithium batteries.

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References

  1. Yang F, Wang D, Zhao Y, Tsui K-L, Bae SJ (2018) A study of the relationship between coulombic efficiency and capacity degradation of commercial lithium-ion batteries. Energy 145:486–495

    Article  Google Scholar 

  2. Hannan MA, Lipu MH, Hussain A, Mohamed A (2017) A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations. Renew Sustain Energy Rev 78:834–854

    Article  Google Scholar 

  3. Lu L, Han X, Li J, Hua J, Ouyang M (2013) A review on the key issues for lithium-ion battery management in electric vehicles. J Power Sources 226:272–288

    Article  Google Scholar 

  4. Ng KS, Moo C-S, Chen Y-P, Hsieh Y-C (2009) Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl Energy 86(9):1506–1511

    Article  Google Scholar 

  5. He H, Zhang X, Xiong R, Xu Y, Guo H (2012) Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles. Energy 39(1):310–318

    Article  Google Scholar 

  6. Yang F, Xing Y, Wang D, Tsui K-L (2016) A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile. Appl Energy 164:387–399

    Article  Google Scholar 

  7. Plett GL (2004) Extended Kalman filtering for battery management systems of LiPB based HEV battery packs: part 2. Modeling and identification. J Power Sources 134(2):262–276

    Article  Google Scholar 

  8. Hu X, Li S, Peng H, Sun F (2012) Robustness analysis of state-of-charge estimation methods for two types of Li-ion batteries. J Power Sources 217:209–219

    Article  Google Scholar 

  9. Xing Y, He W, Pecht M, Tsui KL (2014) State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl Energy 113:106–115

    Article  Google Scholar 

  10. Zou C, Manzie C, Nesic D (2016) A framework for simplification of PDE-based lithium-ion battery models. IEEE Trans Contr Syst Technol 24(5):1594–1609

    Article  Google Scholar 

  11. Salkind AJ, Fennie C, Singh P, Atwater T, Reisner DE (1999) Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology. J Power Sources 80(1e2):293–300

    Article  Google Scholar 

  12. Anton JA, Nieto PG, Viejo CB, Vilan JV (2013) Support vector machines used to estimate the battery state of charge. IEEE Trans Power Electron 28(12):5919–5926

    Article  Google Scholar 

  13. Sahinoglu GO, Pajovic M, Sahinoglu Z, Wang Y, Orlik PV, Wada T (2018) Battery state-of-charge estimation based on regular/recurrent Gaussian process regression. IEEE Trans Ind Electron 65(5):4311–4321

    Article  Google Scholar 

  14. Kang L, Zhao X, Ma J (2014) A new neural network model for the state-of-charge estimation in the battery degradation process. Appl Energy 121:20–27

    Article  Google Scholar 

  15. Yang F, Li W, Li C, Miao Q (2019) State-of-charge estimation of lithium-ion batteries based on gated recurrent neural network. Energy 2:196

    Article  Google Scholar 

  16. Fangfang YS, Zhang WL, Qiang M (2020) State-of-charge estimation of lithium-ion batteries using LSTM and UKF. Energy 201:19067

    Google Scholar 

  17. Chaoui H, Ibe-Ekeocha CC (2017) State of charge and state of health estimation for lithium batteries using recurrent neural networks. IEEE Trans Veh Technol 66(10):8773–8783

    Article  Google Scholar 

  18. Bengio Y, Simard P, Frasconi P (1994) Learning long-term dependencies with gradient descent is difficult. IEEE Trans Neural Netw 5(2):157–166

    Article  Google Scholar 

  19. Yang F, Song X, Xu F, Tsui K-L (2019) State-of-charge estimation of lithium-ion batteries via long short-term memory network. IEEE Access 7:53792–53799

    Article  Google Scholar 

  20. Song X, Yang F, Wang D, Tsui K-L (2019) Combined CNN-LSTM network for state-of-charge estimation of lithium-ion batteries. IEEE Access 7:88894–88902

    Article  Google Scholar 

  21. Chemali E, Kollmeyer PJ, Preindl M, Ahmed R, Emadi A (2018) Long short-term memory networks for accurate state-of-charge estimation of Li-ion batteries. IEEE Trans Ind Electron 65(8):6730–6739

    Article  Google Scholar 

  22. Waldmann T, Wilka M, Kasper M, Fleischhammer M, Wohlfahrt-Mehrens M (2014) Temperature dependent ageing mechanisms in Lithium-ion batteriese A Post Mortem study. J Power Sources 262:129–135

    Article  Google Scholar 

  23. Liu X, Chen Z, Zhang C, Wu J (2014) A novel temperature-compensated model for power Li-ion batteries with dual-particle-filter state of charge estimation. Appl Energy 123:263–272

    Article  Google Scholar 

  24. Liu P, Qiu X, Huang X (2016) Recurrent neural network for text classification with multi-task learning. arXiv preprint arXiv:1605.05101

  25. Kingma DP, Ba J, Adam (2014) A method for stochastic optimization. arXiv preprint arXiv:1412.6980

  26. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from over-fitting. J Mach Learn Res 15(1):1929–1958

    MathSciNet  MATH  Google Scholar 

  27. Jain YK, Bhandare SK (2011) Min-max normalization based data perturbation method for privacy protection. Int J Comput Commun Technol 2(8):45–50

    Google Scholar 

  28. Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. Proc IEEE 92(3):401–422

    Article  Google Scholar 

  29. Julier SJ, Uhlmann JK (1997) New extension of the Kalman filter to nonlinear systems, Signal processing, sensor fusion, and target recognition VI. Int Soc Opt Photon 29:182–93

    Google Scholar 

  30. A123 APR18650m1A cell (xxxx) https://www.batteryspace.com/prod-specs/6612.pdf.

  31. Feng F, Lu R, Zhu C (2014) A combined state of charge estimation method for lithium-ion batteries used in a wide ambient temperature range. Energies 7(5):3004–3032

    Article  Google Scholar 

  32. Wei H, Nicholas W, Chaochao C, Michael P (2014) State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation. Electr Power Energy Syst 62:783–791

    Article  Google Scholar 

  33. Keras (xxxx) TensorFlow Core, homepage: http://www.tensorflow.org

Download references

Acknowledgements

This results was supported by "Regional Innovation Platform(RIP)" through the National Research Foundation of Korea(NRF) funded by the Ministry of Education(MOE)

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  1. Department of Electrical Engineering, Honam University, Gwangsan-gu, Gwangju, Republic of Korea

    Dae-Won Chung, Jae-Ha Ko & Keun-Young Yoon

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  1. Dae-Won Chung
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  2. Jae-Ha Ko
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Correspondence to Keun-Young Yoon.

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Chung, DW., Ko, JH. & Yoon, KY. State-of-Charge Estimation of Lithium-ion Batteries Using LSTM Deep Learning Method. J. Electr. Eng. Technol. 17, 1931–1945 (2022). https://doi.org/10.1007/s42835-021-00954-8

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  • DOI: https://doi.org/10.1007/s42835-021-00954-8

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