A stochastic modelling of electrochemical battery for their lifetime assessment is proposed and numerically evaluated in smart grids context. The method uses a proper battery model, characterized by stochastic current demand, for deducing the battery lifetime. In particular, a stochastic process for describing the current load is adopted, which is namely described by a proper Poisson stochastic process, appearing suitable to describe random smart grid operations. Extensive numerical experiments have been performed by means of this battery model, in order to fit a proper reliability model to numerical lifetime data. Extensive numerical simulations have been performed by adopting, as model parameter values, those obtained after numerous laboratory tests. Hence, it is shown that the Inverse Gaussian model is the best fitting reliability model.
Stochastic Modelling of Electrochemical Batteries for Smart Grids Applications / Chiodo, Elio; L. P., Di Noia; Lauria, Davide; DI NOIA, LUIGI PIO. - (2014), pp. 1059-1063. (Intervento presentato al convegno International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM 2014) tenutosi a Ischia (Italy) nel 18-20 June 2014) [10.1109/SPEEDAM.2014.6872013].
Stochastic Modelling of Electrochemical Batteries for Smart Grids Applications
CHIODO, ELIO;LAURIA, DAVIDE;DI NOIA, LUIGI PIO
2014
Abstract
A stochastic modelling of electrochemical battery for their lifetime assessment is proposed and numerically evaluated in smart grids context. The method uses a proper battery model, characterized by stochastic current demand, for deducing the battery lifetime. In particular, a stochastic process for describing the current load is adopted, which is namely described by a proper Poisson stochastic process, appearing suitable to describe random smart grid operations. Extensive numerical experiments have been performed by means of this battery model, in order to fit a proper reliability model to numerical lifetime data. Extensive numerical simulations have been performed by adopting, as model parameter values, those obtained after numerous laboratory tests. Hence, it is shown that the Inverse Gaussian model is the best fitting reliability model.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
