Li-Ion Battery SoC Estimation Using a Bayesian Tracker McMaster University

Format:

Conference/Event

Author/Creator:

Arasaratnam, Arasaratnam, author.

Contributor:

Ahmed, Ryan

El-Sayed, Mohammed

Habibi, Saeid

Tjong, Jimi

Conference Name:

SAE 2013 World Congress & Exhibition (2013-04-16 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2013

Summary:

Hybrid, plug-in hybrid, and electric vehicles have enthusiastically embraced rechargeable Li-ion batteries as their primary/supplemental power source of choice. Because the state of charge (SoC) of a battery indicates available remaining energy, the battery management system of these vehicles must estimate the SoC accurately. To estimate the SoC of Li-ion batteries, we derive a normalized state-space model based on Li-ion electrochemistry and apply a Bayesian algorithm. The Bayesian algorithm is obtained by modifying Potter's squareroot filter and named the Potter SoC tracker (PST) in this paper. We test the PST in challenging test cases including high-rate charge/discharge cycles with outlier cell voltage measurements. The simulation results reveal that the PST can estimate the SoC with accuracy above 95% without experiencing divergence

Notes:

Vendor supplied data

Publisher Number:

2013-01-1530

Access Restriction:

Restricted for use by site license