Journal of Failure Analysis and Prevention

, Volume 14, Issue 3, pp 412–419 | Cite as

Residual Capacity Estimation for Lead–Acid Batteries Used in Automobiles by the Method of Median Internal Resistance

  • Shang-Kuo Yang
  • Chih-Yung Huang
Technical Article---Peer-Reviewed


The purpose of this study was to investigate the method of residual capacity estimation for lead–acid batteries used in automobiles. First, relation charts for the internal resistances of a battery at various load currents to residual capacity percentages were established, and the relation charts for all load currents were then combined to obtain the corresponding residual capacity by calculating medians. The experimental equipment included lead–acid batteries for automobiles, an electronic loader, an internal resistance tester, and test cables. The experimental procedures were discharging the battery with the electronic loader, using the internal resistance tester to record the internal resistance, voltage, and temperature of the battery, and then transmitting the data to a computer via the test cables for analysis. The experiment obtained nine sets of data, which were recorded in Excel and illustrated using charts. The medians obtained from combining the internal resistance with the residual capacity percentages were used to generate the relation charts for the internal resistances at various load currents to the residual capacity percentages. Finally, 60 Ah was used as the normal capacity to estimate the residual capacity discharging time. Furthermore, a curve-fitting approach for determining the relation equation between internal resistances and capacities was used to replace the table look-up method for residual capacity estimation. The results revealed that the estimation errors after correction were acceptable.


Curve fitting Lead–acid battery Residual capacity estimation Median 


  1. 1.
    E.P. Phillip, H.A. Adnan, Coup de Fouet based VRLA battery capacity estimation. Proceedings of the First IEEE International Workshop on Electronic Design, Test and Applications, Jan 2002, Christchurch, pp. 149–153Google Scholar
  2. 2.
    J.H. Aylor, A. Thieme, B.W. Johnson, A battery state-of-charge indicator for electric wheelchairs. IEEE Trans. Ind. Electron. 39(5), 398–409 (1992)CrossRefGoogle Scholar
  3. 3.
    E.P. Phillip, H.A. Adnan, VRLA battery capacity estimation using soft computing analysis of the Coup de Fouet region. Proceedings of the 22th Telecommunications Energy Conference, Sept 2000, Phoenix, pp. 589–596Google Scholar
  4. 4.
    W.D. Chang, T.R. Chen, A study of the residual capacity estimation for the batteries of motor-driven wheelchairs. Proceedings of the 22th Electrical Power Engineering Symposium, Nov 2001, Kaushung, pp. 965–969Google Scholar
  5. 5.
    H.L. Chan, A new battery model for use with battery energy storage systems and electric vehicles power systems. IEEE Power Eng. Soc. Winter Meet. 1, 470–475 (2000)Google Scholar
  6. 6.
    Z.M. Salameh, M.A. Casacca, W.A. Lynch, A mathematical model for lead–acid batteries. IEEE Trans. Energy Convers. 7(1), 93–98 (1992)CrossRefGoogle Scholar
  7. 7.
    A. Salkind, P. Singh, C. Fennie, D.E. Reisner, A fuzzy logic approach to state-of-charge determination in high performance batteries with applications to electric vehicles. 15th Electric Vehicle Symposium, Oct 1998, Brussels, pp. 13–15Google Scholar
  8. 8.
    X. Sun, Y. Zhong, G. Qi, Z. Nie, EV battery management system with fuzzy expert diagnosing. 16th Electric Vehicle Symposium, Oct 1999, China, pp. 17–19Google Scholar
  9. 9.
    G.E.M.D.C. Bandara, R. Ivanov, S. Gishin, Intelligent fuzzy controller for a lead–acid battery charger. 1999 IEEE International Conference on Systems, Man and Cybernetics, Oct 1999, Tokyo, vol. 6, pp. 185–189Google Scholar
  10. 10.
    G.C. Hsieh, L.R. Chen, K.S. Huang, Fuzzy-controlled Li–ion battery charge system with active state-of-charge controller. IEEE Trans. Ind. Electron. 48(3), 585–593 (2001)CrossRefGoogle Scholar
  11. 11.
    I. Kurisawa, M. Iwata, Internal resistance and deterioration of VRLA battery-analysis of internal resistance obtained by direct current measurement and its application to VRLA battery monitoring technique. 19th Telecommunications Energy Conference, Oct 1997, Melbourne, pp. 687–694Google Scholar
  12. 12.
    I. Sajfar, M. Malaric, R.P. Bullough, Sealed batteries in transient limiting distribution networks-methods of measuring their internal resistance. 12th IEEE Telecommunications Energy Conference, Oct 1990, Orlando, vol. 1, pp. 458–463Google Scholar
  13. 13.
    S. Sato, A. Kawamura, A new estimation method of state of charge using terminal voltage and internal resistance for lead acid battery. IEEE International Conference on Power Conversion, Apr 2002, Osaka, vol. 2, pp. 565–570Google Scholar
  14. 14.
    J. Hirai, T.W. Kim, A. Kawamura, Study on intelligent battery charging using inductive transmission of power and information. IEEE Trans. Power Electron. 15(2), 335–345 (2000)CrossRefGoogle Scholar
  15. 15.
    A. Kawamura, T. Yanagihara, State of charge estimation of sealed lead–acid batteries used for electric vehicles. 29th Annual IEEE on Power Electronics Specialists Conference, May 1998, Fukuoka, vol. 1, pp. 583–587Google Scholar
  16. 16.
    H. Wen-Lung, Residual Capacity Estimation of Batteries in Electric Vehicle with Varying Loads, Master Thesis, Da-Yeh University R.O.C., 2004Google Scholar
  17. 17.
    W.A. Lynch, Z.M. Salameh, Realistic electric vehicle battery evaluation. IEEE Trans. Energy Convers. 12(4), 407–412 (1997)CrossRefGoogle Scholar
  18. 18.
    F.Z. Hong, User Guide for Various Batteries, Quan Hwa, Taipei, 1994. (In Chinese)Google Scholar

Copyright information

© ASM International 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringNational Chin Yi University of TechnologyTaichungTaiwan, ROC

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