Abstract
The degradation of lithium-ion batteries in smart grid and electric vehicle applications is a major drawback, since at some point in their life they become unable to meet minimum performance in terms of supplied voltage and power. Health prognostic is mandatory to ensure safe and reliable operation of batteries, as unsuccessful operation may cause technical/economic detriments or complete failures. The battery Remaining Useful Life (RUL) depends on several random factors and thus it should be probabilistically characterized to allow for decision-making processes. In this paper the Inverse Burr (IB) distribution is proposed to model the RUL, and we develop a hybrid methodology to characterize the RUL even with small amount of data. The IB distribution is applied on a RUL dataset created by Monte Carlo sampling on an electrochemical battery model fitted upon given charge/discharge cycles. Numerical experiments are reported to assess the proposal with respect to benchmark distributions.
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