Life prediction of lithium-ion batteries based on multiscale decomposition and deep learning

doi:10.3785/j.issn.1008-973X.2019.10.002

Journal of ZheJiang University (Engineering Science)

2019, Vol. 53

Issue (10): 1852-1864 DOI: 10.3785/j.issn.1008-973X.2019.10.002

Mechanical and Energy Engineering

Life prediction of lithium-ion batteries based on multiscale decomposition and deep learning

Tian-zhong HU(

),Jian-bo YU*(

)

School of Mechanical Engineering, Tongji University, Shanghai 201804, China

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Abstract

A health degradation prediction model of lithium batteries based on multi-scale deep neural network was proposed aiming at the problems of poor model adaptability and inaccurate prediction in current remaining useful life (RUL) prediction methods. The collected energy data of lithium-ion batteries were decomposed into main trend data and fluctuation data by ensemble empirical mode decomposition (EEMD) and correlation analysis (CA). Then deep belief network (DBN) and long short-term memory (LSTM) were used to model the main trend and fluctuation data respectively. The predicting outcomes of DBN and LSTM were effectively integrated to obtain the health predicted results of lithium-ion battery. The experimental results show that the method can effectively fit the health trend of lithium-ion batteries and obtain accurate RUL prediction results. The performance of the method is better than other typical prediction methods.

Key words： lithium-ion battery remaining useful life (RUL) multiscale analysis deep belief network long short-term memory (LSTM)

Received: 13 May 2019 Published: 30 September 2019

CLC:	TH 165
	TN 911

Corresponding Authors: Jian-bo YU E-mail: 305122638@qq.com;jbyu@tongji.edu.cn

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Cite this article:

Tian-zhong HU,Jian-bo YU. Life prediction of lithium-ion batteries based on multiscale decomposition and deep learning. Journal of ZheJiang University (Engineering Science), 2019, 53(10): 1852-1864.

URL:

http://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2019.10.002 OR http://www.zjujournals.com/eng/Y2019/V53/I10/1852

基于多尺度分解和深度学习的锂电池寿命预测

针对目前的剩余寿命预测（RUL）方法存在模型适应性差及预测不准确等问题，提出多尺度深度神经网络的锂电池健康退化预测模型. 通过经验模态分解（EEMD）方法和相关性分析（CA），将采集到的锂电池能量数据分解为主趋势数据和波动数据；采用深度置信网络（DBN）和长短期记忆网络（LSTM），分别对主趋势与波动数据进行建模；将DBN与LSTM预测结果进行有效集成，得到锂电池的健康预测结果. 实验结果表明，利用该方法能够有效地对锂电池的健康趋势进行拟合，得到准确的RUL预测结果，性能优于其他典型的预测方法.

关键词： 锂电池, 剩余寿命预测 (RUL), 多尺度分析, 深度置信网络, 长短期记忆网络(LSTM)

Fig.1 Life prediction of lithium batteries based on multiscale decomposition and deep learning

Fig.2 Flow chart of training deep belief network

Fig.3 Network structure of long short-term memory

Fig.4 Flow chart of training long short-term memory network

Tab.1 Lithium battery parameters for prediction experiments

Fig.5 Variation curve of battery capacity

Tab.2 Correlation coefficients of intrinsic mode functions

Fig.6 State of health time series based on ensemble empirical modal decomposition

Fig.7 Filtering results of correlation analysis

Tab.3 Parameter setting of deep belief network

Tab.4 Parameter setting of long short-term memory network

Fig.8 Prediction results of models

Fig.9 Integrated prediction results

Tab.5 Running time of prediction algorithm

Tab.6 Lithium battery parameters for high temperature prediction experiments

Fig.10 Prediction results of high temperature environment

Fig.11 One-step prediction results of each integration model

Fig.12 Indicator values of each integration model for one-step prediction

Fig.13 Indicator values of each contrast model for one-step prediction

Fig.14 Five-step prediction results of each integration model

Fig.15 Indicator values of each integration model for five-step prediction

Fig.16 Indicator values of each contrast model for five-step prediction

Fig.17 Prediction results of remaining useful life

Tab.7 RUL prediction results of remaining useful life

Fig.18 Experimental results of uncertainty analysis


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