Back to Search
Start Over
Remaining Useful Life Prediction for Lithium-Ion Batteries Based on Gaussian Processes Mixture
- Source :
- PLoS ONE, Vol 11, Iss 9, p e0163004 (2016), PLoS ONE
- Publication Year :
- 2016
- Publisher :
- Public Library of Science (PLoS), 2016.
-
Abstract
- The remaining useful life (RUL) prediction of Lithium-ion batteries is closely related to the capacity degeneration trajectories. Due to the self-charging and the capacity regeneration, the trajectories have the property of multimodality. Traditional prediction models such as the support vector machines (SVM) or the Gaussian Process regression (GPR) cannot accurately characterize this multimodality. This paper proposes a novel RUL prediction method based on the Gaussian Process Mixture (GPM). It can process multimodality by fitting different segments of trajectories with different GPR models separately, such that the tiny differences among these segments can be revealed. The method is demonstrated to be effective for prediction by the excellent predictive result of the experiments on the two commercial and chargeable Type 1850 Lithium-ion batteries, provided by NASA. The performance comparison among the models illustrates that the GPM is more accurate than the SVM and the GPR. In addition, GPM can yield the predictive confidence interval, which makes the prediction more reliable than that of traditional models.
- Subjects :
- Computer science
Social Sciences
lcsh:Medicine
02 engineering and technology
Machine Learning
Mathematical and Statistical Techniques
Learning and Memory
Kriging
Electrochemistry
0202 electrical engineering, electronic engineering, information engineering
Psychology
lcsh:Science
Multidisciplinary
Covariance
Artificial neural network
Applied Mathematics
Simulation and Modeling
Process (computing)
021001 nanoscience & nanotechnology
Chemistry
Physical Sciences
symbols
0210 nano-technology
Statistics (Mathematics)
Algorithms
Research Article
Computer and Information Sciences
Learning Curves
020209 energy
Lithium
Research and Analysis Methods
symbols.namesake
Electric Power Supplies
Artificial Intelligence
Support Vector Machines
Confidence Intervals
Learning
Statistical Methods
Gaussian process
Artificial Neural Networks
Computational Neuroscience
business.industry
lcsh:R
Cognitive Psychology
Biology and Life Sciences
Computational Biology
Random Variables
Statistical model
Pattern recognition
Models, Theoretical
Probability Theory
Confidence interval
Support vector machine
Cognitive Science
lcsh:Q
Artificial intelligence
business
Mathematics
Forecasting
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 11
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....2e8fd23bfa0694654eb6397a454e8b8e