Sequential Monte Carlo methods for Discharge Time Prognosis in Lithium-Ion Batteries

Marcos E. Orchard, Matías Cerda, Benjamín Olivares, and Jorge F. Silva
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
Supporting Agencies (optional): 
CONICYT (Project FONDECYT 1110070)
AttachmentSizeTimestamp
ijphm_12_010.pdf762.31 KBJanuary 5, 2013 - 7:10pm

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.

Publication Year: 
2012
Publication Volume: 
3
Publication Control Number: 
010
Page Count: 
12
Submission Keywords: 
Energy storage devices
state of charge estimation
state of charge prognosis
particle filtering
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
  
 
 
 

follow us

PHM Society on Facebook Follow PHM Society on Twitter PHM Society on LinkedIn PHM Society RSS News Feed