DAI Junyan,XIA Mingchao,CHEN Qifang.Encoding and Decoding Model of State of Health Estimation and Remaining Useful Life Prediction for Batteries Based on Dual-stage Attention Mechanism[J].Automation of Electric Power Systems,2023,47(6):168-177.
DAI Junyan,XIA Mingchao,CHEN Qifang.Encoding and Decoding Model of State of Health Estimation and Remaining Useful Life Prediction for Batteries Based on Dual-stage Attention Mechanism[J].Automation of Electric Power Systems,2023,47(6):168-177. DOI: 10.7500/AEPS20220615007.
Encoding and Decoding Model of State of Health Estimation and Remaining Useful Life Prediction for Batteries Based on Dual-stage Attention Mechanism
Accurate assessment of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries is extremely important for the safe and stable operation
and the low transparency of the internal operation mechanism of existing prediction models leads to poor assessment reliability. In this paper
an encoding and decoding model of bi-directional long short-term memory network based on dual-stage attention mechanism is proposed for SOH estimation and RUL prediction. The feature attention mechanism on the encoding side and the timing attention mechanism on the decoding side not only improve the model prediction accuracy by dynamically assigning the weights of feature and timing information
but also realize the model interpretability by visualizing the weights. Finally
experimental tests using National Aeronautics and Space Administration (NASA) and Center for Advanced Life Cycle Engineering (CALCE) public battery datasets verify the high accuracy and reliability of the proposed method in this paper.
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SARMAH S B , KALITA P , GARG A , et al . A review of state of health estimation of energy storage systems: challenges and possible solutions for futuristic applications of Li-ion battery packs in electric vehicles [J]. Journal of Electrochemical Energy Conversion and Storage , 2019 , 16 ( 4 ): 1 .
TIAN H X , QIN P L , LI K , et al . A review of the state of health for lithium-ion batteries: research status and suggestions [J]. Journal of Cleaner Production , 2020 , 261 : 120813 .
HANNAN M A , LIPU M S H , HUSSAIN A , et al . A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: challenges and recommendations [J]. Renewable and Sustainable Energy Reviews , 2017 , 78 : 834 - 854 .
LI J F , LYU C , WANG L X , et al . Remaining capacity estimation of Li-ion batteries based on temperature sample entropy and particle filter [J]. Journal of Power Sources , 2014 , 268 : 895 - 903 .
NG M F , ZHAO J , YAN Q Y , et al . Predicting the state of charge and health of batteries using data-driven machine learning [J]. Nature Machine Intelligence , 2020 , 2 ( 3 ): 161 - 170 .
HAN Yunfei , XIE Jia , CAI Tao , et al . Autoencoder-based state evaluation method for lithium-ion battery [J]. Automation of Electric Power Systems , 2021 , 45 ( 24 ): 41 - 48 .
LI Chaoran , XIAO Fei , FAN Yaxiang , et al . An approach to lithium-ion battery SOH estimation based on convolutional neural network [J]. Transactions of China Electrotechnical Society , 2020 , 35 ( 19 ): 4106 - 4119 .
TAN Y D , ZHAO G C . Transfer learning with long short-term memory network for state-of-health prediction of lithium-ion batteries [J]. IEEE Transactions on Industrial Electronics , 2020 , 67 ( 10 ): 8723 - 8731 .
YU Y , HU C H , SI X S , et al . Averaged Bi-LSTM networks for RUL prognostics with non-life-cycle labeled dataset [J]. Neurocomputing , 2020 , 402 : 134 - 147 .
CINAR Y G , MIRISAEE H , GOSWAMI P , et al . Period-aware content attention RNNs for time series forecasting with missing values [J]. Neurocomputing , 2018 , 312 : 177 - 186 .
CHEN J C , CHEN T L , LIU W J , et al . Combining empirical mode decomposition and deep recurrent neural networks for predictive maintenance of lithium-ion battery [J]. Advanced Engineering Informatics , 2021 , 50 : 101405 .
JI Shouling , LI Jinfeng , DU Tianyu , et al . Survey on techniques, applications and security of machine learning interpretability [J]. Journal of Computer Research and Development , 2019 , 56 ( 10 ): 2071 - 2096 .
CARVALHO D V , PEREIRA E M , CARDOSO J S . Machine learning interpretability: a survey on methods and metrics [J]. Electronics , 2019 , 8 ( 8 ): 832 .
YANG C , ZHANG H F , JIANG B , et al . Aspect-based sentiment analysis with alternating coattention networks [J]. Information Processing & Management , 2019 , 56 ( 3 ): 463 - 478 .
LUONG T , PHAM H , MANNING C D . Effective approaches to attention-based neural machine translation [EB/OL]. [ 2022-05-06 ]. https://arxiv.org/abs/1508.04025v3 https://arxiv.org/abs/1508.04025v3 .
CHEN Z H , WU M , ZHAO R , et al . Machine remaining useful life prediction via an attention-based deep learning approach [J]. IEEE Transactions on Industrial Electronics , 2021 , 68 ( 3 ): 2521 - 2531 .
CUI Yu , HOU Huijuan , XU Mingkai , et al . A prediction method for dissolved gas in power transformer oil based on dual-stage attention mechanism [J]. Proceedings of the CSEE , 2020 , 40 ( 1 ): 338 - 347 .
MAMO T , WANG F K . Long short-term memory with attention mechanism for state of charge estimation of lithium-ion batteries [J]. IEEE Access , 2020 , 8 : 94140 - 94151 .
LIU Jianwei , LIU Junwen , LUO Xionglin . Research progress in attention mechanism in deep learning [J]. Chinese Journal of Engineering , 2021 , 43 ( 11 ): 1499 - 1511 .
KHAN A , SARFARAZ A . RNN-LSTM-GRU based language transformation [J]. Soft Computing , 2019 , 23 ( 24 ): 13007 - 13024 .
LIN C P , CABRERA J , YANG F F , et al . Battery state of health modeling and remaining useful life prediction through time series model [J]. Applied Energy , 2020 , 275 : 115338 .
SUN S , SUN J Z , WANG Z L , et al . Prediction of battery SOH by CNN-BiLSTM network fused with attention mechanism [J]. Energies , 2022 , 15 ( 12 ): 4428 .
PATIL M A , TAGADE P , HARIHARAN K S , et al . A novel multistage support vector machine based approach for Li- ion battery remaining useful life estimation [J]. Applied Energy , 2015 , 159 : 285 - 297 .
WANG W , XIA C H , LIU C F , et al . Study of double combination evaluation of urban comprehensive disaster risk [J]. Natural Hazards , 2020 , 104 ( 2 ): 1181 - 1209 .
LIU H Q , WEI Z B , HE W D , et al . Thermal issues about Li-ion batteries and recent progress in battery thermal management systems: a review [J]. Energy Conversion and Management , 2017 , 150 : 304 - 330 .