AUTOMOTIVE BATTERY PROGNOSTICS USING DUAL EXTENDED KALMAN FILTER

被引：0

作者：

Rubagotti, Matteo

Onori, Simona ^{[1
]}

Rizzoni, Giorgio ^{[1
]}

机构：

[1] Ohio State Univ, Ctr Automot Res, Columbus, OH 43210 USA

来源：

PROCEEDINGS OF THE ASME DYNAMIC SYSTEMS AND CONTROL CONFERENCE 2009, PTS A AND B | 2010年

关键词：

MANAGEMENT-SYSTEMS; STATE; PACKS; MODEL; LIFE;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a strategy for estimating the remaining useful life of automotive batteries based on dual Extended Kalman Filter. A nonlinear model of the battery is exploited for the on-line estimation of the State of Charge, and this information is used to evaluate the actual capacity and predict its future evolution, from which an estimate of the remaining useful life is obtained with suitable margins of uncertainty. Simulation results using experimental data from lead-acid batteries show the effectiveness of the approach.

引用

页码：1169 / 1175

页数：7

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