Journal
JOURNAL OF POWER SOURCES
Volume 188, Issue 2, Pages 606-612Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2008.11.143
Keywords
Lead-acid battery; State-of-charge; Estimation; Covariance; Adaptive extended Kalman filter
Funding
- Ministry of Knowledge Economy (MKE)
- Korea Industrial Technology Foundation (KOTEF)
- Brain Korea 21
- Ministry of Knowledge Economy (MKE), Republic of Korea [2008-B01-010] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
- National Research Foundation of Korea [핵C6A1202] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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Lead-acid batteries are widely used in conventional internal-combustion-engined vehicles and in some electric vehicles. In order to improve the longevity, performance, reliability, density and economics of the batteries, a precise state-of-charge (SoC) estimation is required. The Kalman filter is one of the techniques used to determine the SoC. This filter assumes an a priori knowledge of the process and measurement noise covariance values. Estimation errors can be large or even divergent when incorrect a priori covariance values are utilized. These estimation errors can be reduced by using the adaptive Kalman filter, which adaptively modifies the covariance. In this study, an adaptive extended Kalman filter (AEKF) method is used to estimate the SoC. The AEKF can reduce the SoC estimation error, making it more reliable than using a priori process and measurement noise covariance values. (C) 2008 Elsevier B.V. All rights reserved.
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