期刊
ENTROPY
卷 19, 期 4, 页码 -出版社
MDPI
DOI: 10.3390/e19040146
关键词
embedded system; Li-Ion battery; electric; state-of-charge; feed-forward neural network; battery monitoring software
资金
- Karabuk University [KBU-BAP-13/2-DR-007]
- TUBITAK Efficiency Challenge Electric Vehicle
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据