Long Short-Term Memory Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-Ion Batteries

Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology Vol. 67; no. 7; pp. 5695 - 5705
Main Authors: Zhang, Yongzhi, Xiong, Rui, He, Hongwen, Pecht, Michael G.
Format: Journal Article
Language:English
Published: New York IEEE 01.07.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Back propagation
Computer simulation
Data models
deep learning
Degradation
Electric cells
Learning
Life prediction
Lithium
Lithium-ion batteries
Lithium-ion battery
long short-term memory
Mathematical model
Mathematical models
Monte Carlo simulation
Neural networks
Predictive models
Rechargeable batteries
Recurrent neural networks
Reliability analysis
remaining useful life
Support vector machines
Useful life
ISSN:0018-9545, 1939-9359
Online Access:Get full text
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Summary:Remaining useful life (RUL) prediction of lithium-ion batteries can assess the battery reliability to determine the advent of failure and mitigate battery risk. The existing RUL prediction techniques for lithium-ion batteries are inefficient for learning the long-term dependencies among the capacity degradations. This paper investigates deep-learning-enabled battery RUL prediction. The long short-term memory (LSTM) recurrent neural network (RNN) is employed to learn the long-term dependencies among the degraded capacities of lithium-ion batteries. The LSTM RNN is adaptively optimized using the resilient mean square back-propagation method, and a dropout technique is used to address the overfitting problem. The developed LSTM RNN is able to capture the underlying long-term dependencies among the degraded capacities and construct an explicitly capacity-oriented RUL predictor, whose long-term learning performance is contrasted to the support vector machine model, the particle filter model, and the simple RNN model. Monte Carlo simulation is combined to generate a probabilistic RUL prediction. Experimental data from multiple lithium-ion cells at two different temperatures is deployed for model construction, verification, and comparison. The developed method is able to predict the battery's RUL independent of offline training data, and when some offline data is available, the RUL can be predicted earlier than in the traditional methods.
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ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2018.2805189