LE-ELM-based spatiotemporal modeling method of lithium battery thermal process

doi:10.19799/j.cnki.2095-4239.2022.0030

Abstract

Abstract:

The efficiency, life, and safety of lithium batteries are influenced by the lithium battery management system. The control, thermal management, and fault diagnosis of the battery management system depend on the accuracy of the battery's thermal process model. However, the thermal process of the lithium battery is a distributed parameter system with strong nonlinear characteristics. The temperature distribution inside the battery is spatiotemporally coupled and has infinite dimension characteristics, making modeling extremely challenging. To solve these problems, a Laplacian eigenmaps-extreme learning machine (LE-ELM)-based spatiotemporal modeling method for the thermal process of lithium batteries is proposed. First, a local nonlinear dimension reduction method based on LE is developed to learn the spatial basis function to represent the inherent nonlinear topological feature of the original system. Second, a low-dimensional representation of the original data can be generated using time/space separation and the spatial basis functions. Then, ELM is used to approximate the low-order time-series model with the low-dimensional representation and the corresponding current and voltage input signals. Finally, the spatiotemporal temperature distribution can be reconstructed using time/space synthesis. A thermal process of ternary soft-pack lithium battery was modeled using our proposed algorithm to verify the proposed method.

Key words: distributed parameter system, lithium battery thermal process, laplacian eigenmaps (LE), extreme learning machine (ELM)

CLC Number:

TM 912.9

Zhou LYU, Bo HE, Zhenze HUANG, Zhiyong LIANG. LE-ELM-based spatiotemporal modeling method of lithium battery thermal process[J]. Energy Storage Science and Technology, 2022, 11(10): 3200-3208.

Figures/Tables 10

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激活函数	RMSE	时间/s
Sigmoid	0.0017	0.021
tanh	0.0019	0.026
ReLU	0.0079	0.011

方法	训练阶段		测试阶段
方法	时间/s	RMSE	时间/s	RMSE
KL-ELM	4.59	0.0051	0.018	0.9182
LLE-ELM	7.95	0.0018	0.035	0.5606

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