Two-phase separation based spatiotemporal modeling of thermal processes with applications to lithium-ion batteries

被引:10
作者
Wang, Bing-Chuan [1 ]
Feng, Yun [2 ,3 ]
Wang, Shuqiang [4 ]
机构
[1] Cent South Univ, Sch Automat, Changsha 410083, Peoples R China
[2] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Peoples R China
[3] Hunan Univ, Natl Engn Res Ctr Robot Visual Percept & Control, Changsha 410082, Peoples R China
[4] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China
基金
中国国家自然科学基金;
关键词
Spatiotemporal modeling; Time; space separation; Space separation; Thermal process; Lithium-ion battery; PROPER ORTHOGONAL DECOMPOSITION; KARHUNEN-LOEVE DECOMPOSITION; EXTREME LEARNING-MACHINE; MANAGEMENT; REDUCTION;
D O I
10.1016/j.est.2022.104050
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
The thermal process of a battery belongs to distributed parameter systems (DPSs). Modeling of this process is beneficial for energy prediction and conservation. When its first-principle model is not known completely, data driven spatiotemporal modeling is necessary. Because a DPS is infinite-dimensional and time/space coupled, Karhunen-Loeve decomposition (KL) is often used for time/space separation. When modeling a thermal process owning two spatial dimensions, the traditional KL considers two spatial dimensions as a whole without separation. However, in this case, the correlation function, which is critical to time/space separation, would be difficult to evaluate. To remedy this shortcoming, a two-phase separation based spatiotemporal modeling method is proposed. First, one spatial dimension is separated from the process by a set of dominant spatial basis functions (SBFs). Then, the other spatial dimension and the time dimension are separated by another set of dominant SBFs. As a result, the infinite-dimensional time/space coupled process is reduced into a low-dimensional temporal system, which is modeled by radial basis function neural networks. Finally, by spatiotemporal reconstruction, the temperature distribution on the whole space can be predicted. By separating the spatial dimensions, the proposed method is more effective than the traditional KL. Numerical simulations and experiments on the thermal process of a lithium-ion battery have demonstrated the effectiveness of the proposed model.
引用
收藏
页数:9
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