Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group
1
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
2
College of Electronic Science, Northeast Petroleum University, Daqing 163318, China
*
Author to whom correspondence should be addressed.
Academic Editor: Michael Pecht
Energies 2016, 9(5), 387; https://doi.org/10.3390/en9050387
Received: 18 March 2016
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Revised: 27 April 2016
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Accepted: 9 May 2016
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Published: 20 May 2016
(This article belongs to the Special Issue Energy Storage Systems for Plug-in Electric Vehicles and Vehicle to Power Grid Integration)
In a parallel-connected battery group (PCBG), capacity degradation is usually caused by the inconsistency between a faulty cell and other normal cells, and the inconsistency occurs due to two potential causes: an aging inconsistency fault or a loose contacting fault. In this paper, a novel method is proposed to perform online and real-time capacity fault diagnosis for PCBGs. Firstly, based on the analysis of parameter variation characteristics of a PCBG with different fault causes, it is found that PCBG resistance can be taken as an indicator for both seeking the faulty PCBG and distinguishing the fault causes. On one hand, the faulty PCBG can be identified by comparing the PCBG resistance among PCBGs; on the other hand, two fault causes can be distinguished by comparing the variance of the PCBG resistances. Furthermore, for online applications, a novel recursive-least-squares algorithm with restricted memory and constraint (RLSRMC), in which the constraint is added to eliminate the “imaginary number” phenomena of parameters, is developed and used in PCBG resistance identification. Lastly, fault simulation and validation results demonstrate that the proposed methods have good accuracy and reliability.
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Keywords:
parallel-connected battery group; capacity fade; online fault diagnosis; recursive least squares algorithm with restricted memory and constraint; fault simulation
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MDPI and ACS Style
Zhang, H.; Pei, L.; Sun, J.; Song, K.; Lu, R.; Zhao, Y.; Zhu, C.; Wang, T. Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group. Energies 2016, 9, 387. https://doi.org/10.3390/en9050387
AMA Style
Zhang H, Pei L, Sun J, Song K, Lu R, Zhao Y, Zhu C, Wang T. Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group. Energies. 2016; 9(5):387. https://doi.org/10.3390/en9050387
Chicago/Turabian StyleZhang, Hua, Lei Pei, Jinlei Sun, Kai Song, Rengui Lu, Yongping Zhao, Chunbo Zhu, and Tiansi Wang. 2016. "Online Diagnosis for the Capacity Fade Fault of a Parallel-Connected Lithium Ion Battery Group" Energies 9, no. 5: 387. https://doi.org/10.3390/en9050387
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