Lithium-Ion Battery Remaining Useful Life Prognostics Using Data-Driven Deep Learning Algorithm

Data-driven methods are widely applied to predict the remaining useful life (RUL) of lithium-ion batteries, but they generally suffer from two limitations: (i) the potentials of features are not fully exploited, and (ii) the parameters of the prediction model are difficult to determine. To address this challenge, this paper proposes a new data-driven method using feature enhancement and adaptive optimization. First, the features of battery aging are extracted online. Then, the feature enhancement technologies, including the box-cox transformation and the time window processing, are used to fully exploit the potential of features. The box-cox transformation can improve the correlation between the features and the aging status of the battery, and the time window processing can effectively exploit the time information hidden in the historical features sequence. Based on this, gradient boosting decision trees are used to establish the RUL prediction model, and the particle swarm optimization is used to adaptively optimize the model parameters. This method was applied on actual lithium-ion battery degradation data, and the experimental results show that the proposed model is superior to traditional prediction methods in terms of accuracy.

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A Data-driven Auto-CNN-LSTM Prediction Model for Lithium-ion Battery Remaining Useful Life

IEEE Transactions on Industrial Informatics ◽

10.1109/tii.2020.3008223 ◽

2020 ◽

pp. 1-1 ◽

Cited By ~ 1

Author(s):

Lei Ren ◽

Jiabao Dong ◽

Xiaokang Wang ◽

Zihao Meng ◽

Li Zhao ◽

...

Keyword(s):

Prediction Model ◽

Lithium Ion Battery ◽

Lithium Ion ◽

Remaining Useful Life ◽

Data Driven ◽

Useful Life

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Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods

Journal of Power Sources ◽

10.1016/j.jpowsour.2012.11.146 ◽

2013 ◽

Vol 239 ◽

pp. 680-688 ◽

Cited By ~ 202

Author(s):

Adnan Nuhic ◽

Tarik Terzimehic ◽

Thomas Soczka-Guth ◽

Michael Buchholz ◽

Klaus Dietmayer

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion ◽

Remaining Useful Life ◽

Data Driven ◽

Useful Life ◽

Using Data ◽

Health Diagnosis

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A Hybrid Prognostic Approach for Remaining Useful Life Prediction of Lithium-Ion Batteries

Shock and Vibration ◽

10.1155/2016/3838765 ◽

2016 ◽

Vol 2016 ◽

pp. 1-15 ◽

Cited By ~ 2

Author(s):

Wen-An Yang ◽

Maohua Xiao ◽

Wei Zhou ◽

Yu Guo ◽

Wenhe Liao

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion Battery ◽

Learning Algorithm ◽

Lithium Ion ◽

Relevance Vector Machine ◽

Remaining Useful Life ◽

Support Vector ◽

Failure Event ◽

Useful Life ◽

Data Driven Modeling

Lithium-ion battery is a core component of many systems such as satellite, spacecraft, and electric vehicles and its failure can lead to reduced capability, downtime, and even catastrophic breakdowns. Remaining useful life (RUL) prediction of lithium-ion batteries before the future failure event is extremely crucial for proactive maintenance/safety actions. This study proposes a hybrid prognostic approach that can predict the RUL of degraded lithium-ion batteries using physical laws and data-driven modeling simultaneously. In this hybrid prognostic approach, the relevant vectors obtained with the selective kernel ensemble-based relevance vector machine (RVM) learning algorithm are fitted to the physical degradation model, which is then extrapolated to failure threshold for estimating the RUL of the lithium-ion battery of interest. The experimental results indicated that the proposed hybrid prognostic approach can accurately predict the RUL of degraded lithium-ion batteries. Empirical comparisons show that the proposed hybrid prognostic approach using the selective kernel ensemble-based RVM learning algorithm performs better than the hybrid prognostic approaches using the popular learning algorithms of feedforward artificial neural networks (ANNs) like the conventional backpropagation (BP) algorithm and support vector machines (SVMs). In addition, an investigation is also conducted to identify the effects of RVM learning algorithm on the proposed hybrid prognostic approach.

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