Paper Info
Title
Prediction of Lithium battery remaining life based on fuzzy least square support vector regression
Abstract
Batteries are essential components of any aircraft electrical system and exhibit aging and health degradation during operation. Therefore, the correct estimation of the battery remaining useful life (RUL) is important to aircraft operators. The prediction methods of existing Lithium battery remaining life mostly have no learning capabilities and nonlinear prediction ability. In order to predict the remaining life of Lithium battery more accurately, an algorithm based on fuzzy least square support vector regression (FLS-SVR) is presented. This algorithm reconstructs the phase space of multivariate time series using improved embedding dimension time delay automatic algorithm. This algorithm determines the embedding dimension m and the delay timeτ. Then, a FLS-SVR model is built according to m and τ. The parameters of SVR are optimized by adaptive chaotic particle swarm optimization (ACPSO). Comparing with the Logistic regression method, the simulation result demonstrates that the FLS-SVR prediction model has smaller prediction error.
Year
DOI
Venue
2013
10.1109/ICNC.2013.6817943
ICNC
Keywords
Field
DocType
fuzzy set theory,life prediction,support vector regression,regression analysis,multivariate time series phase space,particle swarm optimisation,time delay automatic algorithm,logistic regression method,lithium,battery powered vehicles,aging degradation,delays,fuzzy least square support vector regression,fuzzy least square,least squares approximations,lithium battery remaining life prediction,li,aircraft operators,power engineering computing,fls-svr model,phase space reconstruction,adaptive chaotic particle swarm optimization,acpso,health degradation,aircraft electrical system,remaining life assessment,fls-svr prediction,aircraft power systems,support vector machines,battery remaining useful life,predictive models,optimization,time series analysis,prediction algorithms
Least squares,Embedding,Computer science,Multivariate statistics,Support vector machine,Fuzzy logic,Electric power system,Artificial intelligence,Operator (computer programming),Battery (electricity),Machine learning
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Jing Wan100.34
Qingdong Li210212.25