access icon free State-of-charge estimation with aging effect and correction for lithium-ion battery

This study proposes Lithium-ion battery aging correction state-of-charge (SOC) estimation techniques. Although the battery is aging, the SOC error estimation system maintains the setting range using a low-cost 8 bit micro-controller. The proposed method can track and correct the open-circuit voltage against capacity in the battery management system by comparing the capacity error with the coulomb counting and look-up table methods. The experimental results verify that the SOC estimation error is still lower than 3.5% after 1000 cycles. The SOC estimation verification platform verifies the Sanyo UR18650 W lithium battery. After 300 accelerated aging cycle charge–discharge tests, the test results showed that the SOC prediction precision for an aged battery is as high as 2.67%.

Inspec keywords: ageing; microcontrollers; table lookup; secondary cells; life testing; lithium

Other keywords: lithium-ion battery aging correction; look-up table methods; SOC estimation error; aging effect; accelerated aging cycle charge–discharge tests; SOC error estimation system; SOC estimation verification platform; coulomb counting; state-of-charge estimation; 8 bit micro-controller; capacity error; SOC estimation techniques; open-circuit voltage; Li; battery management system; Sanyo UR18650 W lithium battery

Subjects: Secondary cells; Secondary cells

References

    1. 1)
      • 11. Gharavian, D., Pardis, R., Sheikhan, M.: ‘ZEBRA battery SOC estimation using PSO-optimized hybrid neural model considering aging effect’, IEICE, 2012, ELEX-9, (13), pp. 11151121.
    2. 2)
      • 14. Xidong, T., Xiaofeng, M., Jian, L., Brian, K.: ‘Li-ion battery parameter estimation for state of charge’. Proc. American Control Conf., 2011, pp. 941946.
    3. 3)
    4. 4)
      • 6. Lee, Y.S., Wang, J., Kuo, T.Y.: ‘Lithium ion battery model and fuzzy neural approach for estimating battery state-of-charge’. Proc. 19th Int. Battery, Hybrid and Fuel Cell Electric Vehicle Symp. & Exhibition, 2002, pp. 17891890.
    5. 5)
    6. 6)
    7. 7)
      • 7. Lee, Y.S., Wang, W.Y., Kuo, T.Y.: ‘Soft computing for battery state-of-charge (BSOC) estimation in battery string systems’, IEEE Trans., 2008, IE-55, (1), pp. 229239.
    8. 8)
      • 9. Li, I.H., Wang, W.Y., Su, S.F., Lee, Y.S.: ‘A merged fuzzy neural network and its application in battery state-of-charge estimation’, IEEE Trans., 2007, EC-22, (3), pp. 697708.
    9. 9)
    10. 10)
      • 13. Ranjbar, A.H., Banaei, A., Khoobroo, A., Fahimi, B.: ‘Online estimation of state of charge in lithium-ion batteries using impulse response concept’, IEEE Trans., 2012, SG-3, (1), pp. 360367.
    11. 11)
      • 15. Baronti, F., Fantechi, G., Fanucci, L., et al: ‘State-of-charge estimation enhancing of lithium batteries through a temperature-dependent cell model’. Proc. IEEE Applied Electronics, 2011, pp. 15.
    12. 12)
      • 17. Ng, K.S., Moo, C.S., Chen, Y.P., Hsieh, Y.C.: ‘State-of-charge estimation for lead-acid batteries based on dynamic open-circuit voltage’. Proc. IEEE Second Int. Power and Energy Conf., 2008, pp. 972976.
    13. 13)
      • 12. Hongwen, H., Rui, X., Xiaowei, Z., Fengchun, S., JinXin, F.: ‘State-of-charge estimation of the lithium-ion battery using an adaptive extended Kalman filter based on an improved Thevenin model’, IEEE Trans., 2011, VT-60, (4), pp. 14611469.
    14. 14)
      • 8. Lee, D.T., Shiah, S.J., Lee, C.M., Wang, Y.C.: ‘State-of-charge estimation for electric scooters by using learning mechanisms’, IEEE Trans., 2007, VT-56, (2), pp. 544556.
    15. 15)
      • 10. Li, I.H., Wang, W.Y., Su, S.F., Lee, Y.S.: ‘A novel learning structure of fuzzy neural networks using reduced-form genetic algorithms’. Proc. 12th Int. Conf. Neural Information, 2005, pp. 219223.
    16. 16)
      • 3. Caumont, O., Le Moigne, P., Rombaut, C., Muneret, X., Lenain, P.: ‘Energy gauge for lead-acid batteries in electric vehicles’, IEEE Trans., 2000, EC-15, (3), pp. 354360.
    17. 17)
      • 16. Roscher, M.A., Assfalg, J., Bohlen, O.S.: ‘Detection of utilizable capacity deterioration in battery systems’, IEEE Trans., 2011, VT-60, (1), pp. 98103.
    18. 18)
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