This paper proposes a new methodological approach in the field of studies devoted to proper and accurate selection of a reliability model for battery systems. The study is performed with particular emphasis on the modeling and estimation of the Conditional Reliability Function, conceived as a key analytical tool in predicting the "Remaining useful life" of the battery, which is in turn an important information in order to identify the best maintenance strategy selection, or for inspection optimization, and also spare parts provision. Estimation of the Conditional Reliability Function is developed by means of a method based on the Inverse Gaussian Distribution and its Bayes Estimation. The performances of this estimation are developed and validated by means of extensive simulations and available experimental data. A brief account is reported of robustness analyses of the method with respect to the assumed prior Distribution.
Battery Conditional Reliability Function under an Inverse Gaussian model and its Bayes Estimation / Chiodo, Elio; Lauria, Davide; Mottola, Fabio; Andrenacci, Natascia. - (2019), pp. 550-555. (Intervento presentato al convegno 7th International Conference on Clean Electrical Power, ICCEP 2019 tenutosi a ita nel 2019) [10.1109/ICCEP.2019.8890148].