Battery State of Charge Estimation Using Long Short-Term Memory Network and Extended Kalman Filter

被引：0

作者：

Ni, Zichuan ^{[1
]}

Yang, Ying ^{[1
]}

Xiu, Xianchao ^{[1
]}

机构：

[1] Peking Univ, Coll Engn, Dept Mech & Engn Sci, State Key Lab Turbulence & Complex Syst, Beijing 100871, Peoples R China

来源：

PROCEEDINGS OF THE 39TH CHINESE CONTROL CONFERENCE | 2020年

关键词：

Long short-term memory network; State of charge estimation; Extended Kalman filter; Lithium-ion batteries; OPEN-CIRCUIT-VOLTAGE; LITHIUM-ION BATTERIES;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a long short-term memory network structure is developed to estimate state of charge for lithium-ion batteries owing to its time series characteristic. It is further followed by the extended Kalman filter to alleviate the process noise. The proposed algorithm shows reduced root mean squared error as low as 0.48%, compared with traditional algorithms like linear regression, support vector regression and general shallow neural network. Our work provides a feasible way to estimate state of charge of batteries for general dynamic loading conditions.

引用

页码：5778 / 5783

页数：6

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