2012 Prognostics & System Health Management Conference
(PHM-2012 Beijing)

Prognostics of Lithium-Ion Batteries Using Model-Based and Data-Driven Methods

Chaochao Chen & Michael Pecht

Center for Advanced Life Cycle Engineering (CALCE)
University of Maryland
College Park, MD 20742



This paper presents an integrated approach to predict remaining useful life (RUL) of lithium-ion batteries using model-based and data-driven methods. An empirical model is adopted to emulate the battery degradation trend; real-time measurements are employed to update the model. In order to better deal with prognostics uncertainties arising from many sources in the prediction such as battery unit-to-unit variations, an online model update scheme is proposed in a particle filtering based framework. Filtered data within a moving window are used to adjust the model's parameter values in a real-time manner based on nonlinear least-squares optimization. The proposed approach is studied via experimental data, and the results are discussed.

Keywords: lithium-ion batteries; remaining useful life; model-based; data-driven; model update

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