Open AccessSmart Electricity Meter Prognostics Based on Lithium Battery RUL Prediction
Author(s) -
Ye Chen,
Ziyi Chen,
Ming Zhu,
Yaohua Liao,
Fa Luo,
Xinru Li
Publication year - 2021
Publication title -
distributed generation and alternative energy journal
Language(s) - English
Resource type - Journals
eISSN - 2156-3306
pISSN - 2156-6550
DOI - 10.13052/dgaej2156-3306.3733
Subject(s) - prognostics , reliability engineering , battery (electricity) , automotive engineering , voltage , computer science , state of health , electricity , smart meter , degradation (telecommunications) , robustness (evolution) , engineering , electrical engineering , power (physics) , chemistry , biochemistry , gene , physics , quantum mechanics
Smart Electricity Meters (SEMs) are widely used in distributed generationsystem, and over 67% of its failure are caused by battery low-voltage.Therefore, it is necessary to study the degradation of battery voltage. Thiswork explores the degradation mechanism of lithium battery and proposed touse 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 parametersand enhance the robustness of the model. The Particle Filtering (PF) basedapproach is used in this study to estimate the degradation state such that theRemaining Useful Life (RUL) can be predicted. An accurate prediction canprovide the proper maintenance/replacement schedule for the SEMs beforethe failure occurs.