Advanced mathematical methods of SOC and SOH estimation for lithium-ion batteries

被引：338

作者：

Andre, Dave ^{[1
]}

Appel, Christian ^{[1
]}

Soczka-Guth, Thomas ^{[1
]}

Sauer, Dirk Uwe ^{[2
]}

机构：

[1] Deutsch ACCUmot GmbH & Co KG, D-73230 Kirchheim U Teck, Nabern, Germany

[2] Rhein Westfal TH Aachen, Inst Power Elect & Elect Drives, ISEA, D-52066 Aachen, Germany

来源：

JOURNAL OF POWER SOURCES | 2013年 / 224卷

关键词：

State of health; Lithium-ion battery; State of charge; Battery management system; Unscented Kalman filter; Support vector regression; MANAGEMENT-SYSTEMS; PART; PROGNOSTICS; PACKS; STATE;

D O I：

10.1016/j.jpowsour.2012.10.001

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

Two novel methods to estimate the state of charge (SOC) and state of health (SOH) of a lithium-ion battery are presented. Based on a detailed deduction, a dual filter consisting of an interaction of a standard Kalman filter and an Unscented Kalman filter is proposed in order to predict internal battery states. In addition, a support vector machine (SVM) algorithm is implemented and coupled with the dual filter. Both methods are verified and validated by cell measurements in form of cycle profiles as well as storage and cycle ageing tests. A SOC estimation error below 1% and accurate resistance determination are presented. (C) 2012 Elsevier B.V. All rights reserved.

引用

页码：20 / 27

页数：8

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