contributor author | Hu, Xiaosong | |
contributor author | Yang, Xin | |
contributor author | Feng, Fei | |
contributor author | Liu, Kailong | |
contributor author | Lin, Xianke | |
date accessioned | 2022-02-05T22:12:01Z | |
date available | 2022-02-05T22:12:01Z | |
date copyright | 1/5/2021 12:00:00 AM | |
date issued | 2021 | |
identifier issn | 0022-0434 | |
identifier other | ds_143_06_061001.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4277110 | |
description abstract | Accurate prediction of the remaining useful life (RUL) of lithium-ion batteries can improve the durability, reliability, and maintainability of battery system operation in electric vehicles. To achieve high-accuracy RUL predictions, it is necessary to develop an effective method for long-term nonlinear degradation prediction and quantify the uncertainty of the prediction results. To this end, this paper proposes a hybrid approach for lithium-ion battery RUL prediction based on particle filter (PF) and long short-term memory (LSTM) neural network. First, based on the training set, the model parameters are iteratively updated using the PF algorithm. Second, the LSTM model parameters are obtained using the training set. The mean and standard deviation in the prediction stage are obtained through Monte Carlo (MC) dropout. Finally, the mean value predicted by MC-dropout is used as the measurement for the PF in the prediction phase, the standard deviation represents the uncertainty of the prediction result, and the mean and standard deviation are integrated into the measurement equation of the model. The experimental results show that the proposed hybrid approach has better prediction accuracy than the PF, LSTM algorithm, and two other types of hybrid approaches. The hybrid approach can obtain a narrower confidence interval. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | A Particle Filter and Long Short-Term Memory Fusion Technique for Lithium-Ion Battery Remaining Useful Life Prediction | |
type | Journal Paper | |
journal volume | 143 | |
journal issue | 6 | |
journal title | Journal of Dynamic Systems, Measurement, and Control | |
identifier doi | 10.1115/1.4049234 | |
journal fristpage | 061001-1 | |
journal lastpage | 061001-13 | |
page | 13 | |
tree | Journal of Dynamic Systems, Measurement, and Control:;2021:;volume( 143 ):;issue: 006 | |
contenttype | Fulltext | |