Remaining Useful Life Modeling and Prediction of Rechargeable Batteries

Project: Research

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Traditional reliability assessment methods based on failure time data and data-driven model have been extensively studied in predicting RUL of rechargeable batteries. However, due to the reliance on field data, lack of understanding of interactions of performance parameters and dynamic environments and their effects on systems, there are numerous concerns with these approaches. It is expected that the RUL prediction becomes more accurate when both empirical and physical models in conjunction with individual heterogeneity are incorporated in the modeling and analysis. In this research, we focus on developing a systematic approach for RUL prediction by incorporating additional information and prognostic and health management (PHM) advances into the modeling process. We propose to develop degradation models and estimation methods for reliable RUL prediction of rechargeable batteries, and evaluate the proposed methods using both experimental and field data.


Project number7004665
Effective start/end date1/09/1619/03/19