Lithium battery SOH estimation through FFNN-Reference-Cited by-同舟云学术

Lithium battery SOH estimation through FFNN

Published:2022-04-01 Issue:1 Volume:2260 Page:012034
ISSN:1742-6588
Container-title:Journal of Physics: Conference Series
language:
Short-container-title:J. Phys.: Conf. Ser.

Author:

Ye Zeyu,Liu Wanbo,Wang Xuechao,Zhu Jin,Yin Jingyuan

Abstract

Abstract Abstract:The battery health plays a key and decisive role in the use of lithium batteries. In this paper, the battery data detected offline is used to predict SOH through the big data platform algorithm model, in order to acknowledge the current health of the battery. The construction of the learning model is carried out through the historical data of 1000 batteries data set, so that the MAE(mean absolute error) is lower than 0.095%.

Publisher

IOP Publishing

Subject

General Physics and Astronomy

Link

https://iopscience.iop.org/article/10.1088/1742-6596/2260/1/012034/pdf

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