An Adaptive Fast Charging Strategy for LiFePO4 Battery Applied to Heavy-Haul Train ECP Brake System

Liran Li, Zhiwu Huang, Xiaohui Gong, Jun Peng, and Yanhui Zhou

School of Information Science and Engineering, Central South University
Changsha, Hunan 410075, China

Received:

July 5, 2016

Accepted:

September 13, 2016

Published:

December 20, 2016

Keywords:

SOC estimation, lithium battery, adaptive fast charging strategy, sliding-mode observer

Abstract

A dedicated LiFePO4 battery management system (BMS) used in the electrically controlled pneumatic brake system of a heavy-haul train needs a reliable, efficient and safe charging system to guarantee an uninterrupted power supply. To achieve that, this paper proposes an adaptive fast and safe charging strategy for Li-ion batteries based on the Lyapunov stability theory. In the strategy, a sliding-mode state of charge (SOC) observer is designed, and based on this, the dynamical reference charging current profile is derived. With the estimated SOC and dynamical setting current, the adaptive current control law is proposed by using a Lyapunov function. Finally, experiments are conducted to verify the feasibility and superiority of the proposed method.

Cite this article as:

L. Li, Z. Huang, X. Gong, J. Peng, and Y. Zhou, “An Adaptive Fast Charging Strategy for LiFePO4 Battery Applied to Heavy-Haul Train ECP Brake System,” J. Adv. Comput. Intell. Intell. Inform., Vol.20 No.7, pp. 1077-1085, 2016.

Data files:

References

[1] B. Scrosati and J. Garche, “Lithium batteries: Status, prospects and future,” J. of Power Sources, Vol.195, No.9, pp. 2419-2430, 2010.
[2] X. Gong, Z. Huang, K. Liu, H. Li, and W. Liu, “A lifepo4 battery management system for heavy-haul train electrically controlled pneumatic brake system application,” Proc. of 2015 IEEE Energy Conversion Congress and Exposition (ECCE), IEEE, pp. 1346-1350, 2015.
[3] K. B. Hatzell, A. Sharma, and H. K. Fathy, “A survey of long term health modeling, estimation, and control of lithium-ion batteries: Challenges and opportunities,” Proc. of 2012 American Control Conf (ACC), IEEE, pp. 584-591, 2012.
[4] Y. M. Jeong, Y. K. Cho, J. H. Ahn, and S. H. Ryu, “Enhanced coulomb counting method with adaptive soc reset time for estimating ocv,” Proc. of Energy Conversion Congress and Exposition, pp. 1313-1318, 2014.
[5] G. L. Plett, “Extended kalman filtering for battery management systems of lipb-based hev battery packs: Part 2. modeling and identification,” J. of power sources, Vol.134, No.2, pp. 262-276, 2004.
[6] M. G. Simoes, B. Blunier, and A. Miraoui, “Fuzzy-based energy management control: Design of a battery auxiliary power unit for remote applications,” IEEE Industry Applications Magazine, Vol.20, No.20, pp. 41-49, 2014.
[7] T. Kim, W. Qiao, and L. Qu, “Online soc and soh estimation for multicell lithium-ion batteries based on an adaptive hybrid battery model and sliding-mode observer,” Proc. of 2013 IEEE Energy Conversion Congress and Exposition, IEEE, pp. 292-298, 2013.
[8] Z. Chen, B. Xia, C. C. Mi, and R. Xiong, “Loss-minimization based charging strategy for lithium-ion battery,” IEEE Trans. on Industry Applications, Vol.51, No.5, pp. 4121-4129, 2015.
[9] R. J. Wai, S. J. Jhung, J. J. Liaw, and Y. R. Chang, “Intelligent optimal energy management system for hybrid power sources including fuel cell and battery,” IEEE Trans. on Power Electronics, Vol.28, No.7, pp. 3231-3244, 2013.
[10] M. Gholizadeh and F. R. Salmasi, “Estimation of state of charge, unknown nonlinearities, and state of health of a lithium-ion battery based on a comprehensive unobservable model,” IEEE Trans. on Industrial Electronics, Vol.61, No.3, pp. 1335-1344, 2014.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.