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Entropy 2017, 19(4), 146; doi:10.3390/e19040146

Design and Implementation of SOC Prediction for a Li-Ion Battery Pack in an Electric Car with an Embedded System

1,†
,
2,†
and
1,†,*
1
Department of Mechatronics Engineering, Technology Faculty, Karabük University, Karabük 78050, Turkey
2
Department of Electric and Electronics Engineering, Graduate School of Natural and Applied Sciences, Karabük University, Karabük 78050, Turkey
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Ibrahim Dincer
Received: 21 January 2017 / Revised: 22 March 2017 / Accepted: 27 March 2017 / Published: 17 April 2017
View Full-Text   |   Download PDF [10716 KB, uploaded 17 April 2017]   |  

Abstract

Li-Ion batteries are widely preferred in electric vehicles. The charge status of batteries is a critical evaluation issue, and many researchers are studying in this area. State of charge gives information about how much longer the battery can be used and when the charging process will be cut off. Incorrect predictions may cause overcharging or over-discharging of the battery. In this study, a low-cost embedded system is used to determine the state of charge of an electric car. A Li-Ion battery cell is trained using a feed-forward neural network via Matlab/Neural Network Toolbox. The trained cell is adapted to the whole battery pack of the electric car and embedded via Matlab/Simulink to a low-cost microcontroller that proposed a system in real-time. The experimental results indicated that accurate robust estimation results could be obtained by the proposed system. View Full-Text
Keywords: embedded system; Li-Ion battery; electric; state-of-charge; feed-forward neural network; battery monitoring software embedded system; Li-Ion battery; electric; state-of-charge; feed-forward neural network; battery monitoring software
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Soylu, E.; Soylu, T.; Bayir, R. Design and Implementation of SOC Prediction for a Li-Ion Battery Pack in an Electric Car with an Embedded System. Entropy 2017, 19, 146.

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