NDLI: Estimating state of charge and state of health of rechargable batteries on a per-cell basis

Content Provider	IEEE Xplore Digital Library
Author	Mills, A. Zambreno, J.
Copyright Year	2015
Description	Author affiliation: Iowa State Univ., Ames, IA, USA (Mills, A.; Zambreno, J.)
Abstract	Much of current research on State-of-Charge (SOC) and State-of-Health (SOH) tracking for rechargeable batteries such as Li-ion focuses primarily on analyzing single cells, or otherwise treat a set of series-connected cells as a homogeneous unit. Since no two cells have precisely the same properties, for applications involving large batteries this can severely limit the accuracy and utility of the approach. In this paper we develop an model-driven approach using a Dual Unscented Kalman Filter to allow a Battery Monitoring System (BMS) to monitor in real time both SOC and SOH of each cell in a battery. A BMS is an example of a Cyber-Physical System (CPS) which requires deep understanding of the behavior of the physical system (i.e., the battery) in order to obtain reliability in demanding applications. In particular, since the SOH of a cell changes extremely slowly compared to SOC, our dual filter operates on two timescales to improve SOH tracking. We show that the use of the Unscented Kalman Filter instead of the more common Extended Kalman Filter simplifies the development of the system model equations in the multiscale case. We also show how a single “average” cell model can be used to accurately estimate SOH for different cells and cells of different ages.
Starting Page	1
Ending Page	6
File Size	431043
Page Count	6
File Format	PDF
ISBN	9781479973576
e-ISBN	9781479973583
DOI	10.1109/MSCPES.2015.7115399
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-13
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Current measurement Estimation System-on-chip Batteries Mathematical model Kalman filters
Content Type	Text
Resource Type	Article

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