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Internal temperature prediction of ternary polymer lithium‐ion battery pack based on CNN and virtual thermal sensor technology
Author(s) -
Wang Mengyi,
Hu Weifeng,
Jiang Yanfang,
Su Fang,
Fang Zheng
Publication year - 2021
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.6699
Subject(s) - ternary operation , battery (electricity) , battery pack , lithium ion battery , mean squared error , approximation error , thermometer , thermal , computer science , materials science , algorithm , mathematics , physics , thermodynamics , statistics , power (physics) , programming language
Summary In order to achieve real‐time prediction of the battery internal temperature via the external temperature measured, a method for predicting internal temperature of a ternary polymer lithium‐ion battery pack based on convolutional neural networks (CNN) and virtual thermal sensor (VTS) was proposed in this paper. A 128‐channel thermometer was used to measure the internal (64 uniformly distributed points) and external (64 uniformly distributed points) temperature of the lithium‐ion battery pack during seven discharge cycles for a total of 81 376 sets of data. The external temperature measured was used as the input of CNN and the internal temperature predicted as the output of CNN. CNN compared with linear regression (LR) to verify the difference of prediction accuracy. Mean square error (MSE), mean absolute error (MAE), max‐error (MAXE), and goodness of fit ( R 2 ‐score) were used to evaluate the prediction accuracy. The results showed that the proposed method can accurately predict real‐time temperature with the MSE as 0.047. In addition, this method does not require any knowledge of battery thermal properties, heat generation, or thermal boundary conditions.