Lithium-Ion Battery Life Prediction Method under Thermal Gradient Conditions

被引:1
|
作者
Song, Dawei [1 ]
Wang, Shiqian [1 ]
Di, Li [2 ]
Zhang, Weijian [2 ]
Wang, Qian [3 ]
Wang, Jing V. [3 ]
机构
[1] State Grid Henan Elect Power Econ & Technol Res In, Zhengzhou 450052, Peoples R China
[2] State Grid Henan Elect Power Co, Internet Dept, Zhengzhou 450052, Peoples R China
[3] Wuhan Univ Technol, Sch Automat, Wuhan 430070, Peoples R China
基金
中国国家自然科学基金;
关键词
thermal gradient; capacity degradation; life prediction; extreme learning machine; sparrow search algorithm;
D O I
10.3390/en16020767
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
Thermal gradient is inevitable in a lithium-ion battery pack because of uneven heat generation and dissipation, which will affect battery aging. In this paper, an experimental platform for a battery cycle aging test is built that can simulate practical thermal gradient conditions. Experimental results indicate a high nonlinear degree of battery degradation. Considering the nonlinearity of Li-ion batteries aging, the extreme learning machine (ELM), which has good learning and fitting ability for highly nonlinear, highly nonstationary, and time-varying data, is adopted for prediction. A battery life prediction model based on the sparrow search algorithm (SSA) is proposed in this paper to optimize the random weights and bias of the ELM network and verified by experimental data. The results show that compared with traditional ELM and back-propagation neural networks, the prediction results of ELM optimized by SSA have lower mean absolute error percentages and root mean square errors, indicating that the SSA-ELM model has higher prediction accuracy and better stability and has obvious advantages in processing data with a high nonlinear degree.
引用
收藏
页数:13
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