期刊
INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
卷 62, 期 -, 页码 783-791出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2014.04.059
关键词
Neural networks; Unscented Kalman filter; State of charge estimation; Lithium ion batteries; Electric vehicles; Battery management systems
资金
- National Science Foundation of the United States [1234451]
- National Science Foundation of China [71231001]
- Directorate For Engineering
- Div Of Civil, Mechanical, & Manufact Inn [1234451] Funding Source: National Science Foundation
Lithium-ion batteries have been widely used as the energy storage systems in personal portable electronics (e.g. cell phones, laptop computers), telecommunication systems, electric vehicles and in various aerospace applications. To prevent the sudden loss of power of battery-powered systems, there are various approaches to estimate and manage the battery's state of charge (SOC). In this paper, an artificial neural network-based battery model is developed to estimate the SOC, based on the measured current and voltage. An unscented Kalman filter is used to reduce the errors in the neural network-based SOC estimation. The method is validated using LiFePO4 battery data collected from the Federal Driving Schedule and dynamical stress testing. (C) 2014 Elsevier Ltd. All rights reserved.
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