Abstract
A model of a lead-acid battery is presented with an equivalent circuit and the parameters are determined with experiments. An inductor is added into the circuit with the consideration from the output of impedance spectrum. Extended Kalman filter for the nonlinear system is used to estimate three state variables and further to calculate the state-of-charge. The algorithm is simplified and it can be implemented for the real-time estimation with an error less than 1 % in the testing with the SOC range between 60 % and 100 %. The testing is also conducted with a battery not fully charged and the estimation error is higher, close to 2 %.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Sabatiera, J., Aouna, M., Oustaloupa, A., Grégoireb, G., Ragotb F., Royb, P., Fractional system identification for lead acid battery state of charge estimation. Signal Processing, 2006. 86(10): p. 2645–2657.
Cai, C., Du, D., Liu, Z., Ge, J. State-of-charge (soc) estimation of high power Ni-Mh rechargeable battery with artificial neural network. in Proceedings of the 9th International Conference on Neural Information Processing (ICONIP’OZ). 2002.
Piller, S., Perrin, M., Jossen, A., Methods for state-of-charge determination and their applications. Journal of Power Sources, 2011. 96: p. 113–120.
Rodriguesa, S., Munichandraiahb, N., Shuklaa, A.K., A review of state-of-charge indication of batteries by means of a.c. impedance measurements. Journal of Power Sources, 2000. 87: p. 12–20.
Huet, F., A review of impedance measurements for determination of the state-of-charge or state-of-health of secondary batteries. Journal of Power Sources, 1998. 70: p. 59–69.
Chan, C.C., Lo, E.W.C., Shen, W., The available capacity computation model based on artificial neural network for lead–acid batteries in electric vehicles. Journal of Power Sources, 2000. 87: p. 201–204.
Lee, J., Nam, O., Cho, B.H., Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering. Journal of Power Sources, 2007. 174: p. 9–15.
Salameh, Z.M., A mathematical model for lead-acid batteries. Energy Conversion, IEEE Transactions on, 1992. 7: p. 93–98.
Zhan, C.-J., Two electrical models of the lead-acid battery used in a dynamic voltage restorer Generation, Transmission and Distribution, IEE Proceedings-, 2003. 150: p. 175–182.
Bhangu, B., Bentley, P., Stone, D. A., Bingham, C., Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2005. 54(3): p. 783–794.
Vasebi, A., Partovibakhsh, M., Bathaee, S., A novel combined battery model for state-of-charge estimation in lead-acid batteries based on extended Kalman filter for hybrid electric vehicle applications. Journal of Power Sources, 2007. 174: p. 30-40.
Perez, R., Lead-Acid Battery State of Charge vs. Voltage. Home Power, 1993: p. 66–69.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Zhang, L., Xiong, X. (2015). The SOC Estimation of a Lead Acid Rechargeable Battery. In: Sobh, T., Elleithy, K. (eds) Innovations and Advances in Computing, Informatics, Systems Sciences, Networking and Engineering. Lecture Notes in Electrical Engineering, vol 313. Springer, Cham. https://doi.org/10.1007/978-3-319-06773-5_67
Download citation
DOI: https://doi.org/10.1007/978-3-319-06773-5_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06772-8
Online ISBN: 978-3-319-06773-5
eBook Packages: EngineeringEngineering (R0)