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Sequential Monte Carlo Methods for Discharge Time Prognosis in Lithium-ion Batteries

Author(s): Marcos E. Orchard | Matías Cerda | Benjamín Olivares | Jorge F. Silva

Journal: International Journal of Prognostics and Health Management
ISSN 2153-2648

Volume: 3;
Issue: 2;
Start page: 27;
Date: 2012;
Original page

Keywords: Energy storage devices state of charge estimation state of charge prognosis particle filtering

This paper presents the implementation of a particle-filtering-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 state-space 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 particle-filtering-based framework that considers a statistical characterization of uncertainty for future discharge profiles.

Tango Jona
Tangokurs Rapperswil-Jona

RPA Switzerland

RPA Switzerland

Robotic process automation