Title: Capacity estimation method of lithium-ion batteries based on deep convolution neural network

Authors: Renwang Song; Lei Yang; Linying Chen; Zengshou Dong

Addresses: College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' College of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

Abstract: In order to ensure the safe and reliable operation of lithium-ion battery packs, the battery management system needs to be able to monitor the health states of each single battery in the packs. In this paper, a deep convolution neural network is used to estimate capacity of batteries. First, the main structure of the deep convolution neural network is introduced. It includes five-layer convolutional structures and three-layer fully connected structures. Due to the local connectivity and weight sharing of deep convolution neural network, the model can accurately estimate the capacity according to the measured values in the charging and discharging process. Then, the model is used to estimate the capacity of four cells of NASA. The average root mean square error is 0.033Ah and the prediction accuracy is more than 95%. It is proved that the proposed capacity prediction model can achieve high accuracy and robustness in application cases.

Keywords: lithium-ion batteries; state of health; capacity estimation; deep learning.

DOI: 10.1504/IJBIC.2022.126788

International Journal of Bio-Inspired Computation, 2022 Vol.20 No.2, pp.119 - 125

Received: 20 Nov 2021
Accepted: 29 Jan 2022
Published online: 07 Nov 2022 *

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