A novel prediction method based on the support vector regression for the remaining useful life of lithium-ion batteries

Remaining useful life (RUL) prediction has great importance in prognostics and health management (PHM). Relaxation effect refers to the capacity regeneration phenomenon of lithium-ion batteries during a long rest time, which can lead to a regenerated useful time (RUT). This paper mainly studies the influence of the relaxation effect on the degradation law of lithium-ion batteries, and proposes a novel RUL prediction method based on Wiener processes. This method can simplify the modeling complexity by using the RUT to model the recovery process. First, the life cycle of a lithium-ion battery is divided into the degradation processes that eliminate the relaxation effect and the recovery processes caused by relaxation effect. Next, the degradation model, after eliminating the relaxation effect, is established based on linear Wiener processes, and the model for RUT is established by using normal distribution. Then, the prior parameters estimation method based on maximum likelihood estimation and online updating method under the Bayesian framework are proposed. Finally, the experiments are carried out according to the degradation data of lithium-ion batteries published by NASA. The results show that the method proposed in this paper can effectively improve the accuracy of RUL prediction and has a strong engineering application value.

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Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter

Journal of Power Sources ◽

10.1016/j.jpowsour.2014.07.176 ◽

2014 ◽

Vol 271 ◽

pp. 114-123 ◽

Cited By ~ 107

Author(s):

Hancheng Dong ◽

Xiaoning Jin ◽

Yangbing Lou ◽

Changhong Wang

Keyword(s):

Particle Filter ◽

Support Vector Regression ◽

Lithium Ion Battery ◽

Health Monitoring ◽

Life Prediction ◽

Lithium Ion ◽

Remaining Useful Life ◽

Support Vector ◽

State Of Health ◽

Useful Life

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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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