This paper presents the implementation of a particlefiltering- based prognostic framework that allows estimating the state-of-charge (SOC) and predicting the discharge time of energy storage devices (more specifically lithium-ion batteries). The proposed approach uses an empirical statespace model inspired in the battery phenomenology and particle-filtering to study the evolution of the SOC in time; adapting the value of unknown model parameters during the filtering stage and enabling fast convergence for the state estimates that define the initial condition for the prognosis stage. SOC prognosis is implemented using a particlefiltering- based framework that considers a statistical characterization of uncertainty for future discharge profiles.
particle filtering, state of charge estimation, Energy storage devices, state of charge prognosis
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