As one of the most widely used energy storage systems, lithium-ion batteries are attracting more and more attention, and the estimation of lithium-ion batteries remaining useful life (RUL) becoming a critical problem. Generally, RUL can be predicted in two ways: physics of failure (PoF) method and data driven method. Due to the internal electro-chemical reactions are either inaccessible to sensors or hard to measure; the data-driven method is adopted because it does not require specific knowledge of material properties. In this paper, three data-driven algorithms, i. e., Support Vector Machine (SVM), Autoregressive Moving Average (ARMA), and Particle Filtering (PF) are presented for RUL prediction. The lithium-ion battery aging experiment data set has been trained to implement simulation. Based on the RUL prediction result, we can conclude that: (1) ARMA model achieved better result than SVM, however, the result shows a linear trend, which fail to properly reflect the degradation trend of the battery; (2) SVM often suffers from over fitting problem and is more suitable for single-step prediction; and (3) PF approach achieved a better prediction and reflected the trends of degradation of the battery owing to its combined with specific model.
Analysis of Data-Driven Prediction Algorithms for Lithium-Ion Batteries Remaining Useful Life
2013
6 Seiten
Aufsatz (Konferenz)
Englisch
Algorithmus , Vorhersagetheorie , Teilchen (Partikel) , Partikelfilter , Lithium-Ionen-Akkumulator , gleitender Mittelwert , Ansteuerungstechnik , experimentelle Daten , Lithium , Datenanalyse , elektrochemische Reaktion , Lithiumbatterie , Energiespeicher , ARMA-Modell , SVM (Support Vector Machine) , Support Vector Machine
A novel health indicator for on-line lithium-ion batteries remaining useful life prediction
Online Contents | 2016
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