Capacity Estimation of Lithium-ion Battery Based on Deep Learning Under Dynamic Conditions

被引：0
|
作者：
Bi G. [1 ]
Xie X. [1 ]
Cai Z. [1 ]
Luo Z. [1 ]
Chen C. [1 ]
Zhao X. [1 ]
机构：
[1] School of Electric Power Engineering, Kunming University of Science and Technology, Kunming
来源：
Qiche Gongcheng/Automotive Engineering | 2022年 / 44卷 / 06期
关键词：
Capacity estimation; CNN; Deep learning; GRU-RNN; Lithium-ion battery;
D O I：
10.19562/j.chinasae.qcgc.2022.06.008
中图分类号：
TM912 [蓄电池];
学科分类号：
摘要：
In the aging course of lithium-ion battery, nonlinear complicated changes occur inside the battery, therefore directly using the operation data of lithium-ion battery such as current, voltage and temperature in certain time sections to conduct real-time estimation on the battery state of health under dynamic condition is a challenging issue. In this paper, the random charging and discharging data of lithium-ion battery are selected, the time and frequency features of some segments of dynamic data are extracted to compose time and frequency feature matrices as input, a cascaded convolutional neural network and a gated recurrent unit capacity estimation model are constructed to extract the intrinsic features of input data, and the related features of each time sequence are further explored to fulfill the estimation of battery capacity under dynamic condition. The results of experimental verification utilizing NASA's lithium-ion battery random use data set show that the method adopted can accurately estimate the capacity of lithium-ion battery under the condition of only the nominal capacity of battery is known. Finally, the effects of the setting of model's hyper-parameters, the time-sequence length of raw data, network input and model structure on the accuracy of battery capacity estimation are analyzed. © 2022, Society of Automotive Engineers of China. All right reserved.
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页码：868 / 877and885
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