Open AccessRemaining Useful Life Prediction of Lithium-ion Battery based on Attention Mechanism with Positional Encoding
Author(s) -
Beitong Zhou,
Cheng Cheng,
Guijun Ma,
Yong Zhang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/895/1/012006
Subject(s) - computer science , encoding (memory) , recurrent neural network , artificial intelligence , deep learning , mechanism (biology) , battery (electricity) , transformer , convolutional neural network , artificial neural network , machine learning , voltage , engineering , philosophy , power (physics) , physics , epistemology , quantum mechanics , electrical engineering
The rising demands of more reliable and stable electrical systems attach importance to accurate Remaining Useful Life (RUL) prediction of the lithium-ion batteries. As artificial intelligence and machine learning techniques advance, data-driven methods especially deep learning algorithms have become the rising star in RUL prediction. Recurrent Neural Networks (RNNs) and their variants such as Long Short Term Memory have proven effectiveness in various sequential tasks. However, due to its iterative nature along the time axis, RNNs take much time for information to flow through the network for prediction. Inspired by recent advance brought by Transformer in sequence transduction tasks, we proposed the attention mechanism based Convolutional Neural Network (CNN) with positional encoding to tackle this problem. The attention mechanism enables the network to focus on specific parts of sequences and positional encoding injects position information while utilizing the parallelization merits of CNN on GPUs. Empirical experiments show that the proposed approach is both time effective and accurate in battery RUL prediction.