期刊
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
卷 66, 期 2, 页码 280-293出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIM.2016.2622838
关键词
Kernel smoothing (KS); lithium (Li)-ion batteries; particle learning (PL); particle number adjustment; remaining useful life (RUL) estimation
资金
- National Natural Science Foundation of China [61672430, 61522207, 61573284]
- NWPU Basic Research Fund [3102016JKBJJGZ08]
- Open Research Foundation of the State Key Laboratory of Digital Manufacturing Equipment & Technology, Huazhong University of Science Technology [DMETKF2015009]
- Fund of National Engineering and Research Center for Commercial Aircraft Manufacturing [SAMC14-JS-15-045]
As an important part of prognostics and health management, accurate remaining useful life (RUL) prediction for lithium (Li)-ion batteries can provide helpful reference for when to maintain the batteries in advance. This paper presents a novel method to predict the RUL of Li-ion batteries. This method is based on the framework of improved particle learning (PL). The PL framework can prevent particle degeneracy by resampling state particles first with considering the current measurement information and then propagating them. Meanwhile, PL is improved by adjusting the number of particles at each iteration adaptively to reduce the running time of the algorithm, which makes it suitable for online application. Furthermore, the kernel smoothing algorithm is fused into PL to keep the variance of parameter particles invariant during recursive propagation with the battery prediction model. This entire method is referred to as PLKS in this paper. The model can then be updated by the proposed method when new measurements are obtained. Future capacities are iteratively predicted with the updated prediction model until the predefined threshold value is triggered. The RUL is calculated according to these predicted capacities and the predefined threshold value. A series of case studies that demonstrate the proposed method is presented in the experiment.
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