Particle-Filtering-Based Discharge Time Prognosis for Lithium-Ion Batteries With a Statistical Characterization of Use Profiles

• Published in: IEEE transactions on reliability 2015-06, Vol.64 (2), p.710-720
• Main Authors: Pola, Daniel A., Navarrete, Hugo F., Orchard, Marcos E., Rabie, Ricardo S., Cerda, Matias A., Olivares, Benjamin E., Silva, Jorge F., Espinoza, Pablo A., Perez, Aramis
• Format: Article
• Language: English
• Subjects: Batteries; Battery charge measurement; Current measurement; Discharges (electric); Lithium-ion battery; Markov chain; particle filtering; Prognostics and health management; state-of-charge prognosis; System-on-chip; Voltage measurement
• ISSN: 0018-9529; 1558-1721
• DOI: 10.1109/TR.2014.2385069

We present the implementation of a particle-filtering-based prognostic framework that utilizes statistical characterization of use profiles to (i) estimate the state-of-charge (SOC), and (ii) predict the discharge time of energy storage devices (lithium-ion batteries). The proposed approach uses a novel empirical state-space model, inspired by battery phenomenology, and particle-filtering algorithms to estimate SOC and other unknown model parameters in real-time. The adaptation mechanism used during the filtering stage improves the convergence of the state estimate, and provides adequate initial conditions 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 based on maximum likelihood estimates of transition probabilities for a two-state Markov chain. All algorithms have been trained and validated using experimental data acquired from one Li-Ion 26650 and two Li-Ion 18650 cells, and considering different operating conditions.