Temperature Prediction of Lithium-Ion Batteries Based on Physical Information and Deep Neural Network

doi:10.11930/j.issn.1004-9649.202403004

Abstract

Abstract:

Accurately predicting the temperature of lithium-ion batteries is a key technology for battery management systems. A deep neural network is constructed for temperature prediction of lithium-ion batteries based on their dynamic as well as time-dependent characteristics. The model can extract the potential high-dimension features of the data and appropriately reduce their dimensionality to reduce the model complexity while capturing the long-term dependence of temperature through the layer of long short-term memory cells. In addition, the heat generation rate is calculated in real-time through the open circuit voltage, terminal voltage and current of the lithium-ion battery, thus providing additional physical information input to the deep neural network. The results show that the method has better temperature prediction performance compared to other methods.

Key words: lithium-ion batteries, temperature prediction, heat generation rate, physical information, deep neural network.

Laien CHEN, Xiaoyong ZENG, Zihao ZENG, Caichen CHENG, Yaoke SUN. Temperature Prediction of Lithium-Ion Batteries Based on Physical Information and Deep Neural Network[J]. Electric Power, 2024, 57(11): 18-25.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202403004

https://www.electricpower.com.cn/EN/Y2024/V57/I11/18

Figures/Tables 8

Fig.1 Voltage, current, SOC, and temperature curves for US06

Fig.2 The framework of temperature prediction

Fig.3 The unit of 1 D convolution layer

Fig.4 The structure of LSTM unit

Fig.5 The result of four different driving cycles

Table 1 Error indicators for four driving drive cycles

驾驶驱动循环	E_rms/℃	E_ma/℃	E_max/℃
HWFET	0.10	0.08	1.27
LA92	0.26	0.21	1.38
UDDS	0.14	0.10	1.27
US06	0.39	0.29	1.52

Fig.6 Comparison with different methods

Table 2 Errors of different methods (HWFET)

方法	E_rms/℃	E_ma/℃	E_max/℃
BP	0.79	0.54	3.62
GRU	0.18	0.14	1.21
DNN（无产热率）	0.13	0.10	1.61
DNN	0.10	0.08	1.27

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