基于EEMD-GRU-NN锂离子电池表面温度预测方法研究

doi:10.19799/j.cnki.2095-4239.2024.0571

储能科学与技术 ›› 2025, Vol. 14 ›› Issue (1): 380-387.doi: 10.19799/j.cnki.2095-4239.2024.0571

基于EEMD-GRU-NN锂离子电池表面温度预测方法研究

叶石丰¹(), 洪朝锋², 綦晓²(), 吴伟雄², 谭子健¹, 周奇¹, 张兆阳¹

^1.广东电网有限责任公司广州供电局，广东广州 510620
^2.暨南大学能源电力研究中心，广东珠海 519070

收稿日期:2024-06-25 修回日期:2024-07-15 出版日期:2025-01-28 发布日期:2025-02-25
通讯作者: 綦晓 E-mail:sephyip@tom.com;qixiao.jnu@gmail.com
作者简介:叶石丰（1978—），男，本科，工程师，研究方向为储能控制与优化，E-mail：sephyip@tom.com；
基金资助:
南方电网科技项目（GDKIXM20230246(030100KC23020017);国家自然科学基金(52106244);广东省基础与应用基础研究基金(2022A1515011936)

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

Shifeng YE¹(), Chaofeng HONG², Xiao QI²(), Weixiong WU², Zijian TAN¹, Qi ZHOU¹, Zhaoyang ZHANG¹

^1.Guangzhou Power Supply Bureau of Guangdong Power Grid Co. , Ltd. , Guangzhou 510620, Guangdong, China
^2.Energy and Electricity Research Center Jinan University, Zhuhai 519070, Guangdong, China

Received:2024-06-25 Revised:2024-07-15 Online:2025-01-28 Published:2025-02-25
Contact: Xiao QI E-mail:sephyip@tom.com;qixiao.jnu@gmail.com

摘要/Abstract

摘要：

随着全球可持续能源需求的持续增加，储能电池的安全性愈发重要。准确预测电池温度可以防止电池过热，避免因温度过高导致的电池故障、起火或爆炸，从而提高设备的安全性。为此，本研究提出一种基于集合经验模态分解(ensemble empirical mode decomposition，EEMD)的门控循环单元(gated recurrent unit, GRU)和基础神经网络(neural network, NN)联合预测方法。首先，利用EEMD将锂电池温升数据分解为周期分量和趋势分量，并将其作为监督学习的离线训练目标值；然后，结合电池温度特性选取合适的特征参数作为模型的输入特征，针对分解得到的不同分量，分别构建基于GRU和NN的实时在线预测模型；最后，将两种模型的输出叠加作为最终预测结果，并通过与常见神经网络模型的比较，证明了所提出方法的准确性。实验结果表明，在常温下，本研究提出的方法在各个评价指标上均优于常见模型，预测结果的均方根误差为0.10 ℃，平均绝对误差为0.075 ℃，最大误差为0.34 ℃。此外，在极端环境下，模型的预测能力有所下降，但其误差仍在合理范围内，证明了该模型在极端条件下仍具有较好的适应能力。

关键词: 锂离子电池, 温度预测, 集合经验模态分解, 门控循环单元

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

中图分类号:

TM 912

叶石丰, 洪朝锋, 綦晓, 吴伟雄, 谭子健, 周奇, 张兆阳. 基于EEMD-GRU-NN锂离子电池表面温度预测方法研究[J]. 储能科学与技术, 2025, 14(1): 380-387.

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.

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参考文献 19

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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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基于EEMD-GRU-NN锂离子电池表面温度预测方法研究

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

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