Life prediction of lithium battery based on EEMD-GSGRU

doi:10.19799/j.cnki.2095-4239.2020.0022

Abstract

Abstract:

Mathematical models for predicting lithium battery lives are complex, prone to over-fitting, and have poor generalization; accordingly, a time series decomposition-integration model (EEMD-GSGRU) based on Ensemble Empirical Mode Decomposition (EEMD) and Gated Recurrent Unit (GRU) with Grid Search (GS) is proposed. In this model, the lithium battery capacity data are first decomposed into the trend factor with a large total proportion and the error factor with a small total proportion. Then, the decomposed time series are predicted for GRU and combined for a real-time rolling prediction. Finally, GS is used to search the network parameters and Adam optimization is used to update the network weight of the GRU. Using a lithium battery dataset provided by NASA for the model, the superiority of the EEMD-GSGRU model is proved in comparison to other algorithms. It is shown that the EEMD-GSGRU model improves the accuracy of the lithium battery life prediction.

Key words: lithium battery, life prediction, signal decomposition, gated recurrent unit, prediction accuracy

CLC Number:

TM 912.9

Lingzhi YI, Zongguang ZHANG, Chaodong FAN, Xianguang LUO, Wang LI, Wenhan LIU. Life prediction of lithium battery based on EEMD-GSGRU[J]. Energy Storage Science and Technology, 2020, 9(5): 1566-1573.

Figures/Tables 12

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References 14

1	刘江永, 刘文翰, 易灵芝. 多时序协同中期负荷预测模型[J]. 电力系统及其自动化学报, 2020, 32(2): 48-53.
	LIU Jiangyong, LIU Wenhan, YI Lingzhi. Multi-sequence coordinated medium-term load forecasting model[J]. Proceedings of the CSU-EPSA, 2020, 32(2): 48-53.
2	张凝, 徐皑冬, 王锴, 等. 基于粒子滤波算法的锂离子电池剩余寿命预测方法研究[J]. 高技术通讯, 2017, 27(8): 699-707.
	ZHANG Ning, XU Aidong, WANG Kai, et al. Research on prediction of the remaining useful life of lithium-ion batteries based on particle filtering[J]. Chinese High Technology Letters, 2017, 27(8): 699-707.
3	张朝龙, 何怡刚, 袁莉芬. 基于EEMD和MKRVM的锂电池剩余寿命预测方法[J]. 电力系统及其自动化学报, 2018, 30(7): 38-44.
	ZHANG Chaolong, HE Yigang, YUAN Lifen. Prediction approach for remaining useful life of lithium-ion battery based on EEMD and MKRVM[J]. Proceedings of the CSU-EPSA, 2018, 30(7): 38-44.
4	刘月峰, 赵光权, 彭喜元. 多核相关向量机优化模型的锂电池剩余寿命预测方法[J]. 电子学报, 2019, 47(6): 1285-1292.
	LIU Yuefeng, ZHAO Guangquan, PENG Xiyuan. A lithium-ion battery remaining using life prediction method based on multi-kernel relevance vector machine optimized model[J]. Acta Electronica Sinica, 2019, 47(6): 1285-1292.
5	马彦, 陈阳, 张帆, 等. 基于扩展H_∞粒子滤波算法的动力电池寿命预测方法[J]. 机械工程学报, 2019, 55(20): 36-43.
	MA Yan, CHEN Yang, ZHANG Fan, et al. Remaining useful life prediction of power battery based on extend H_∞ particle filter algorithm[J]. Journal of Mechanical Engineering, 2019, 55(20): 36-43.
6	王一宣, 李泽滔. 基于改进支持向量回归机的锂离子电池剩余寿命预测[J]. 汽车技术, 2020(2): 28-32.
	WANG Yixuan, LI Zetao. Prediction for lithium-ion battery remaining useful life based on improved SVR[J]. Automobile Technology, 2020(2): 28-32.
7	WU Z H, HUANG N E. Ensemble empirical mode decomposition: A noise assisted data analysis method[J]. Advances in Adaptive Data Analysis, 2009, 1(1): 1-41.
8	赵健, 樊彦国, 张音. 基于EEMD-BP组合模型的区域海平面变化多尺度预测[J]. 系统工程理论与实践, 2019, 39(10): 2713-2722.
	ZHAO Jian, FAN Yanguo, ZHANG Yin. Multi-scale prediction of regional sea level variations based on EEMD-BP combined model[J]. Systems Engineering —Theory & Practice, 2019, 39(10): 2713-2722.
9	邓带雨, 李坚, 张真源, 等. 基于EEMD-GRU-MLR的短期电力负荷预测[J]. 电网技术, 2020, 44(2): 593-602.
	DENG Daiyu, LI Jian, ZHANG Zhenyuan, et al. Short-term electric load forecasting based on EEMD-GRU-MLR[J]. Power System Technology, 2019, 55(20): 36-43.
10	赵健, 樊彦国, 张音. 基于EEMD-BP组合模型的区域海平面变化多尺度预测[J]. 系统工程理论与实践, 2019, 39(10): 2713-2722.
	ZHAO Jian, FAN Yanguo, ZHANG Yin. Multi-scale prediction of regional sea level variation based on EEMD-BP combined model[J]. Systems Engineering-Theory & Practice, 2019, 39(10): 2713-2722.
11	MO Baohua, YU Jingsong, TANG Diyin, et al. A remaining useful life prediction approach for lithium-ion batteries using Kalman filter and an improved particle filter[C]//IEEE International Conference on Prognostics & Health Management, IEEE, 2016.
12	GAN Dahua, WANG Yi, ZANG Ning, et al．Enhancing short-term probabilistic residential load forecasting with quantile long-short-term memory[J]. The Journal of Engineering, 2017(14): 2622-2627.
13	罗伟林, 张立强, 吕超, 等. 锂离子电池寿命预测国外研究现状综述[J]. 电源学报, 2013(1): 140-144.
	LUO Weilin, ZHANG Liqiang, Chao LYU, et al. Review on foreign status of life prediction of lithium-ion batteries[J]. Journal of Power Supply, 2013(1): 140-144.
14	MA Yan, CHEN Yang, ZHOU Xiuwen, et al. Remaining useful life prediction of lithium-ion battery based on Gauss-Hermite particle filter[J]. IEEE Transactions on Control Systems Technology, 2019, 27(4): 1788-1795.

最优值	15	3	1	0.0006
参数	隐藏神经元数	输入维度	输出维度	学习率
最优值	20	1	300	Adam
参数	批次大小	时间步长	迭代次数	优化策略

数据集	误差类别	种类	模型误差
B5	RMSE	平均值	0.0169
		最大值	0.0417
		最小值	0.0001
	MAPE	平均值	1.1921
		最大值	2.9296
		最小值	0.0080
B6	RMSE	平均值	0.0309
		最大值	0.1307
		最小值	0.0003
	MAPE	平均值	2.2706
		最大值	8.9347
		最小值	0.0216
B7	RMSE	平均值	0.0111
		最大值	0.0362
		最小值	0.0007
	MAPE	平均值	0.7279
		最大值	2.1899
		最小值	0.0467

数据集	种类	SVM	LSTM	GRU	EEMD-GSGRU
B5	平均值	0.0806	0.0511	0.0510	0.0169
B5	最大值	0.1455	0.0911	0.0958	0.0417
B6	平均值	0.0804	0.0866	0.0817	0.0309
B6	最大值	0.1803	0.1757	0.1653	0.1307
B7	平均值	0.0679	0.0475	0.0428	0.0111
B7	最大值	0.1269	0.0888	0.0879	0.0362

数据集	种类	SVM	LSTM	GRU	EEMD-GSGRU
B5	平均值	5.6790	3.7087	3.7014	1.1921
B5	最大值	10.1562	6.6109	6.8739	2.9296
B6	平均值	6.0625	6.5081	6.1657	2.2706
B6	最大值	13.5135	13.2178	12.5286	8.9347
B7	平均值	4.4735	3.1919	2.8822	2.1899
B7	最大值	8.3059	5.9646	5.8233	0.0467

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