Support Vector Machines Used to Estimate the Battery State of Charge

The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO _{4} ) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many ap...

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Bibliographic Details
Published in:IEEE transactions on power electronics 2013-12, Vol.28 (12), p.5919-5926
Main Authors: Alvarez Anton, Juan Carlos, Garcia Nieto, Paulino Jose, Blanco Viejo, Cecilio, Vilan Vilan, Jose Antonio
Format: Article
Language:English
Subjects:
Accuracy
Batteries
Current measurement
Discharges (electric)
Electric currents
Electric vehicles
Lithium batteries
modeling
state of charge (SOC)
Support vector machines
support vector machines (SVMs)
support vector regression (SVR)
System-on-a-chip
Temperature measurement
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Summary:The aim of this study is to estimate the state of charge (SOC) of a high-capacity lithium iron manganese phosphate (LiFeMnPO _{4} ) battery cell from an experimental dataset using a support vector machine (SVM) approach. SVM is a type of learning machine based on statistical learning theory. Many applications require accurate measurement of battery SOC in order to give users an indication of available runtime. It is particularly important for electric vehicles or portable devices. In this paper, the proposed SOC estimator extracts model parameters from battery charging/discharging testing cycles, using cell current, cell voltage, and cell temperature as independent variables. Tests are carried out on a 60 Ah lithium-ion cell with the dynamic stress test cycle to set up the SVM model. The SVM SOC estimator maintains a high level of accuracy, better than 6% over all ranges of operation, whether the battery is charged/discharged at constant current or it is operating in a variable current profile.
ISSN:0885-8993
1941-0107
DOI:10.1109/TPEL.2013.2243918