Lithium battery SOH estimation through FFNN. Issue 1 (1st April 2022)

• Record Type: Journal Article
• Authors: Ye, Zeyu; Liu, Wanbo; Wang, Xuechao; Zhu, Jin; Yin, Jingyuan
• Is Part Of: Journal of physics. Volume 2260:Issue 1(2022)
• Publication Date: 2022-04-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2260/1/012034
• Languages: English
• ISSNs: 1742-6588

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%.