Remaining useful life assessment of lithium-ion batteries in implantable medical devices

JOURNAL OF POWER SOURCES

卷:375

Article

Hu, Chao^1,2  Ye, Hui³  Jain, Gaurav³  Schmidt, Craig³ 

[1] Iowa State Univ, Dept Mech Engn, Ames, IA 50011 USA

[2] Iowa State Univ, Dept Elect & Comp Engn, Ames, IA 50011 USA

[3] Medtron Energy & Component Ctr, Brooklyn Ctr, MN 55430 USA

关键词: Capacity;    Health monitoring;    Prognostics;    Remaining useful life;    Lithium-ion battery;   

DOI  :  10.1016/j.jpowsour.2017.11.056

来源: Elsevier

【 摘 要 】

This paper presents a prognostic study on lithium-ion batteries in implantable medical devices, in which a hybrid data-driven/model-based method is employed for remaining useful life assessment. The method is developed on and evaluated against data from two sets of lithium-ion prismatic cells used in implantable applications exhibiting distinct fade performance: 1) eight cells from Medtronic, PLC whose rates of capacity fade appear to be stable and gradually decrease over a 10-year test duration; and 2) eight cells from Manufacturer X whose rates appear to be greater and show sharp increase after some period over a 1.8-year test duration. The hybrid method enables online prediction of remaining useful life for predictive maintenance/control. It consists of two modules: 1) a sparse Bayesian learning module (data-driven) for inferring capacity from charge-related features; and 2) a recursive Bayesian filtering module (model-based) for updating empirical capacity fade models and predicting remaining useful life. A generic particle filter is adopted to implement recursive Bayesian filtering for the cells from the first set, whose capacity fade behavior can be represented by a single fade model; a multiple model particle filter with fixed-lag smoothing is proposed for the cells from the second data set, whose capacity fade behavior switches between multiple fade models.

【 授权许可】

Free