Smart Electricity Meter Prognostics Based on Lithium Battery RUL Prediction
DOI:
https://doi.org/10.13052/dgaej2156-3306.3733Keywords:
Smart electricity meters, lithium battery, remaining useful life, particle filtering.Abstract
Smart Electricity Meters (SEMs) are widely used in distributed generation
system, and over 67% of its failure are caused by battery low-voltage.
Therefore, it is necessary to study the degradation of battery voltage. This
work explores the degradation mechanism of lithium battery and proposed to
use voltage as degradation index to estimate the health status of the system.
Four groups of batteries of the same type and batch are used for the test.
The purpose is to use multiple sets of data to train the model parameters
and enhance the robustness of the model. The Particle Filtering (PF) based
approach is used in this study to estimate the degradation state such that the
Remaining Useful Life (RUL) can be predicted. An accurate prediction can
provide the proper maintenance/replacement schedule for the SEMs before
the failure occurs.
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