史永胜, 施梦琢, 丁恩松, 洪元涛, 欧阳. 基于CEEMDAN–LSTM组合的锂离子电池寿命预测方法[J]. 工程科学学报, 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 引用本文: 史永胜, 施梦琢, 丁恩松, 洪元涛, 欧阳. 基于CEEMDAN–LSTM组合的锂离子电池寿命预测方法[J]. 工程科学学报, 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 SHI Yong-sheng, SHI Meng-zhuo, DING En-song, HONG Yuan-tao, OU Yang. Combined prediction method of lithium-ion battery life based on CEEMDAN–LSTM[J]. Chinese Journal of Engineering, 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 Citation: SHI Yong-sheng, SHI Meng-zhuo, DING En-song, HONG Yuan-tao, OU Yang. Combined prediction method of lithium-ion battery life based on CEEMDAN–LSTM[J]. Chinese Journal of Engineering , 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 史永胜, 施梦琢, 丁恩松, 洪元涛, 欧阳. 基于CEEMDAN–LSTM组合的锂离子电池寿命预测方法[J]. 工程科学学报, 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 引用本文: 史永胜, 施梦琢, 丁恩松, 洪元涛, 欧阳. 基于CEEMDAN–LSTM组合的锂离子电池寿命预测方法[J]. 工程科学学报, 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 SHI Yong-sheng, SHI Meng-zhuo, DING En-song, HONG Yuan-tao, OU Yang. Combined prediction method of lithium-ion battery life based on CEEMDAN–LSTM[J]. Chinese Journal of Engineering, 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 Citation: SHI Yong-sheng, SHI Meng-zhuo, DING En-song, HONG Yuan-tao, OU Yang. Combined prediction method of lithium-ion battery life based on CEEMDAN–LSTM[J]. Chinese Journal of Engineering , 2021, 43(7): 985-994. doi: 10.13374/j.issn2095-9389.2020.06.30.007 针对目前锂离子电池寿命预测结果不准确的问题，提出了一种多模态分解的锂离子电池组合预测模型，从而学习锂离子电池退化过程的微小变化。该方法在单一长短期记忆（LSTM）预测模型的基础上，采用了自适应噪声完全集成的经验模态分解(CEEMDAN)算法将锂电池容量分为主退化趋势和若干局部退化趋势，然后使用长短期记忆神经网络（LSTMNN）算法分别对所分解的若干退化数据进行寿命预测，最后将若干预测结果进行有效集成。结果表明，所提出的CEEMDAN−LSTM锂离子电池组合预测模型最大平均绝对百分比误差不超过1.5%，平均相对误差在3%以内，且优于其他预测模型。

电池健康管理 /  锂离子电池 /  剩余使用寿命 /  长短期记忆神经网络 /  自适应噪声完全集成经验模态分解 Abstract: As a new generation of new energy battery, lithium-ion battery is widely used in various fields, including electronic products, electric vehicles, and power supply, due to its advantages of high energy density, light weight, long cycle life, small self-discharge, no memory effect, and no pollution. With the wide application of lithium-ion battery, numerous research on its performance has been done, including its health assessment as one of the hot spots. Repeated charging and discharging of a lithium-ion battery that was run under full charge state results to internal irreversible chemical changes leading to a fall in the maximum available capacity. Specifically, a decline to 70%–80% of the rated capacity results in lithium-ion battery failure. Battery failure may lead to electrical equipment damage, resulting in safety accidents. Therefore, it is of great significance to predict the remaining usable life of lithium-ion battery for improving system reliability. In this paper, a combination prediction model for lithium-ion batteries with multimode decomposition was presented based on the long and short-term memory (LSTM) prediction model to learn about small changes in its degradation process. A complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm was used to divide the capacity into main degradation trend and some local degradation trend. Long Short-Term Memory Neural Network (LSTMNN) algorithm was then introduced to perform the capacity prediction of decomposed degradation data. Finally, some prediction results were integrated effectively. The maximum mean absolute percentage error (MAPE) of the proposed CEEMDAN–LSTM lithium-ion battery combination prediction model does not exceed 1.5%. The average relative error is less than 3%, which is better than the other prediction model.

Key words: battery health management /  lithium-ion batteries /  remaining useful life /  long- and short-term memory neural network /  complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)  Number of iterationsNumber of hidden layersNumber of hidden cellsInitial learning rate 50012000.002 ModelBatteryRUL _tr RUL _pr RUL _er P _er LSTMCS3319820130.0152CS34176269930.5284CS3716716920.0120CS38201223220.1095CX3619119210.0076CX3722422730.0134EMD–LSTMCS3319819800CS3417618370.0398CS3716716920.0114CS38201222210.1045CX3619119210.0062CX3722422510.0045CEEMDAN–
LSTMCS3319819710.0051CS3417617600CS3716717140.0239CS38201223220.1095CX3619119210.0062CX3722422400 ModelBatteryRUL _tr RUL _pr RUL _er P _er LSTMCS33323346230.0712CS34301325240.0797CS373475271800.5187CS38381408270.0709CX3638138540.0105CX3741441950.0121EMD–LSTMCS3332332410.0031CS3430130430.0100CS3734735470.2018CS38381408270.0708CX36381430490.1286CX3741441400CEEMDAN–
LSTMCS3332332300CS3430130980.0266CS3734735360.0173CS38381406250.0656CX3638137650.0131CX3741441400 Zhang C L, He Y G, Yuan L F. Prediction approach for remaining useful life of lithium-ion battery based on EEMD and MKRVM. Proc CSU–EPSA , 2018, 30(7): 38 doi: 10.3969/j.issn.1003-8930.2018.07.006

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. 电力系统及其自动化学报, 2018, 30(7):38 doi: 10.3969/j.issn.1003-8930.2018.07.006 Qi H M. Research on Prediction Method of Remaining Life of Lithium Battery Based on Deep Learning [Dissertation]. Harbin: Harbin Institute of Technology, 2019

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