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Mathematical Problems in Engineering
Volume 2017, Article ID 7835049, 9 pages
Research Article

Capacity Fast Prediction and Residual Useful Life Estimation of Valve Regulated Lead Acid Battery

1College of Information System and Management, National University of Defense Technology, Changsha 410073, China
2School of Mechanical and Electrical Engineering, Shandong Jianzhu University, Jinan 250000, China

Correspondence should be addressed to Tianyu Liu; nc.ude.tdun@uynaituil

Received 14 June 2016; Accepted 27 December 2016; Published 20 February 2017

Academic Editor: Dane Quinn

Copyright © 2017 Qin He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


The usable capacity of acid lead batteries is often used as the degradation feature for online RUL (residual useful life) estimation. In engineering applications, the “standard” fully discharging method for capacity measure is quite time-consuming and harmful for the high-capacity batteries. In this paper, a data-driven framework providing capacity fast prediction and RUL estimation for high-capacity VRLA (valve regulated lead acid) batteries is presented. These batteries are used as backup power sources on the ships. The relationship between fully discharging time and partially discharging voltage curve is established for usable capacity extrapolation. Based on the predicted capacity, the particle filtering approach is utilized to obtain battery RUL distribution. A case study is conducted with the experimental data of GFM-200 battery. Results confirm that our method not only reduces the prediction time greatly but also performs quite well in prediction accuracy of battery capacity and RUL.