Deep neural network battery charging curve prediction using 30 points collected in 10 min

Jinpeng Tian, Rui Xiong, Weixiang Shen, Jiahuan Lu, Xiao Guang Yang

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Accurate degradation monitoring over battery life is indispensable for the safe and durable operation of battery-powered applications. In this work, we extend conventional capacity degradation estimation to the estimation of entire constant-current charging curves. A deep neural network (DNN) is developed to estimate complete charging curves by featuring small portions of the charging curves to form the input. We demonstrate that the charging curves can be accurately captured with an error of less than 16.9 mAh for 0.74 Ah batteries with 30 points collected in less than 10 min. Validation based on batteries working at different current rates and temperatures further demonstrates the effectiveness of the proposed method. This method also enjoys the advantage of transfer learning; that is, a DNN trained on one battery dataset can be used to improve the curve estimation of other batteries operating under different scenarios by using few training data.

Original languageEnglish (US)
Pages (from-to)1521-1534
Number of pages14
JournalJoule
Volume5
Issue number6
DOIs
StatePublished - Jun 16 2021

All Science Journal Classification (ASJC) codes

  • Energy(all)

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