Lithium-ion batteries surface temperature prediction toward EEMD-GRU-NN method

doi:10.19799/j.cnki.2095-4239.2024.0571

Abstract

Abstract:

As global demand for sustainable energy increases, ensuring the safety of energy storage batteries has become crucial. Accurate prediction of battery temperature is essential for preventing overheating and reducing the risk of battery failure, fire, or explosion due to high temperatures, thereby improving device safety. This study introduces a combined prediction approach based on ensemble empirical mode decomposition, gated recurrent units, and a basic neural network (NN). Initially, lithium battery temperature data was decomposed into periodic and trend components, which serve as target values for offline supervised learning training. Next, suitable feature parameters based on the temperature characteristics of the battery were selected as input features for the model to create a real-time online prediction model. Finally, the outputs of the two models were superimposed to obtain the final prediction result. We demonstrated the accuracy of the proposed method by comparing it with common NN models. Experimental results indicate that under normal temperature conditions, the proposed method outperforms traditional models in all evaluation metrics, achieving a root mean square error of 0.10℃, an average absolute error of 0.075℃, and a maximum error of 0.34℃. Although the prediction capability of the model decreases under extreme conditions, the error remains within a reasonable range, confirming the robustness of the model under extreme conditions.

Key words: lithium-ion battery, temperature prediction, ensemble empirical mode decomposition, gated recurrent unit

CLC Number:

TM 912

Shifeng YE, Chaofeng HONG, Xiao QI, Weixiong WU, Zijian TAN, Qi ZHOU, Zhaoyang ZHANG. Lithium-ion batteries surface temperature prediction toward EEMD-GRU-NN method[J]. Energy Storage Science and Technology, 2025, 14(1): 380-387.

Figures/Tables 11

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模型指标	RNN	LSTM	GRU	RNN-NN	LSTM-NN	GRU-NN
RMSE	0.8381	0.6940	0.6323	0.1645	0.1153	0.1028
MAE	0.6608	0.5369	0.4717	0.1180	0.0848	0.0748
MAXE	1.8879	1.6625	1.6270	0.6073	0.4117	0.3355

误差指标	10 ℃	0 ℃	-10 ℃	-20 ℃
RMSE	0.1021	0.2415	0.1649	0.2806
MAE	0.0770	0.1892	0.1314	0.2257
MAXE	0.4991	0.6465	0.5459	0.8506

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