Prediction of Battery Discharge States Based on the Recurrent Neural Network

被引：2

作者：

Hsieh, Yi-Zeng ^{[1
,2
,3
]}

Tan, Shih-Wei ^{[1
]}

Gu, Siang-Long ^{[1
]}

Jeng, Yu-Lin ^{[4
]}

机构：

[1] Natl Taiwan Ocean Univ, Dept Elect Engn, Keelung, Taiwan

[2] Natl Taiwan Ocean Univ, Inst Food Safety & Risk Management, Keelung, Taiwan

[3] Natl Taiwan Ocean Univ, Ctr Excellence Ocean Engn, Keelung, Taiwan

[4] Southern Taiwan Univ Sci & Technol, Dept Informat Management, Tainan, Taiwan

来源：

JOURNAL OF INTERNET TECHNOLOGY | 2020年 / 21卷 / 01期

关键词：

Deep learning; Battery life prediction; LSTM; RNN; GRU; OF-CHARGE;

D O I：

10.3966/160792642020012101011

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The recurrent neural network can solve the time sequential problems and the battery discharge state is predicted based on the time sequential neural network. The main purpose is to predict the battery discharge condition with recurrent neural network, and then improve the traditional mathematical prediction method. Nowadays, prediction of the battery life cycle is more important. Compared with our models, there are the five fixing currents as testing experiments. The error rate has less than 2% and the prediction battery life is close to real data.

引用

页码：113 / 120

页数：8

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