Analysis of Data-Driven Prediction Algorithms for Lithium-Ion Batteries Remaining Useful Life

Abstract:

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.

• Periodical: Advanced Materials Research (Volume 717)
• Pages: 390-395
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.717.390
• Online since: July 2013
• Authors: Lin Jiang, Wei Ming Xian, Bin Long, Hou Jun Wang
• Keywords: Algorithm (GA), Autoregressive Moving Average, Data-Driven, Lithium-Ion Battery, Particle Filter (PF), Prediction, Remaining Useful Life, Support Vector Machine (SVM)

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