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Capacity Prediction and Validation of Lithium-Ion Batteries Based on Long Short-Term Memory Recurrent Neural Network
2020
IEEE Access
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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. ...
As a kind of deep learning network, long short-term memory recurrent neural network (LSTM-RNN) is employed to solve the problem with long-term dependences. ...
INDEX TERMS Lithium-ion batteries, capacity prediction, aging factors, long short-term memory (LSTM).
NOMENCLATURE
A. ABBREVIATIONS
I. ...
doi:10.1109/access.2020.3025766
fatcat:vf2tks6a7vbhvhytzbxzpmtnue
Lithium-ion Battery Remaining Useful Life Prediction with Long Short-term Memory Recurrent Neural Network
2017
Proceedings of the Annual Conference of the Prognostics and Health Management Society, PHM
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A novel RUL prediction approach based on Long Short-term Memory (LSTM) Recurrent Neural Network (RNN) is proposed in this paper. ...
LSTM is able to capture long-term dependencies and model sequential data among the capacity degradation of lithium-ion batteries. ...
ACKNOWLEDGEMENT This research was supported by China Scholarship Fund and Guangxi Key Laboratory of Automatic Detecting Technology and Instruments (YQ17202). ...
doi:10.36001/phmconf.2017.v9i1.2447
fatcat:342dhbw47fbwlao7egi4nu5u3e
State of Health Estimation for Lithium-Ion Battery Based on Long Short Term Memory Networks
2019
DEStech Transactions on Environment Energy and Earth Science
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In this paper, a state of health (SOH) estimation method using long short term memory (LSTM) networks is applied to predict battery life for electric vehicles (EVs). ...
The discharging time under a constant current, the number of charging and discharging cycles, and the charging capacity are employed to build the prediction model with LSTM networks. ...
The long short term memory (LSTM) model is a special form of recurrent neural network, which was proposed by [19] . ...
doi:10.12783/dteees/iceee2018/27855
fatcat:2zct3mxvz5ebhkdugocvzbuu6e
Attention-Based Long Short-Term Memory Recurrent Neural Network for Capacity Degradation of Lithium-Ion Batteries
2021
Batteries
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To improve the prediction accuracy of lithium-ion battery capacity degradation, we propose a hybrid long short-term memory recurrent neural network model with an attention mechanism. ...
Using public battery datasets, the proposed model is compared to some published models, and it gives better prediction performance in terms of mean absolute percentage error and root mean square error. ...
[6] proposed long short-term memory (LSTM) recurrent neural network for the remaining useful life prediction of lithium-ion batteries. However, Li et al. ...
doi:10.3390/batteries7040066
fatcat:yy6ixvz4pbhunkwcdw6b76ffnm
A novel state-of-health prediction method based on long short-term memory network with attention mechanism for lithium-ion battery
2022
Frontiers in Energy Research
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Accordingly, this paper proposes a novel SOH prediction method for lithium-ion batteries based on the long short-term memory (LSTM) neural network combined with attention mechanism (AM). ...
The state-of-health (SOH) of lithium-ion batteries is one of the important core issues of battery management systems (BMS). ...
Acknowledgments The authors would like to acknowledge the support of the Shandong Provincial Demonstration Base for the Joint Cultivation of Graduates with Integrated Production and Education "New Generation ...
doi:10.3389/fenrg.2022.972486
fatcat:3qcdwfjelfgenchgjgp2bdib7y
Real-time parameter estimation of an electrochemical lithium-ion battery model using a long short-term memory network
2020
IEEE Access
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INDEX TERMS Electrochemical battery model, lithium-ion battery, long short-term memory, real-time parameter estimation, recurrent neural network, synthetic data generation. ...
In order to efficiently learn the dynamic characteristics of a lithium-ion battery, a well-known recurrent neural network, called a long short-term memory model, is employed with other techniques such ...
Specifically, as one of the most representative and widely used RNNs [21] , the long short-term memory (LSTM) model is employed to explicitly avoid the long-term dependency problem and maintain good performance ...
doi:10.1109/access.2020.2991124
fatcat:indslghqsbhttbo6gxpfxnb7fy
Recurrent Neural Networks for Estimating the State of Health of Lithium-Ion Batteries
2024
Batteries
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The model was designed for smartphone battery swap applications utilizing Gated Recurrent Unit (GRU) neural networks. ...
This paper proposes a model for estimating the SoH curve of lithium-ion batteries using the state of charge curve. ...
Acknowledgments: The authors thank for the financial support provided by two Brazilian Funding Agencies (CNPq and CAPES) and the R&D program of the Brazilian Electricity Regulatory Agency (ANEEL) in partnership ...
doi:10.3390/batteries10030111
fatcat:t4hb3tttareopf2jmyc75whoj4
State of Charge Estimation Method for Lithium-Ion Batteries in All-Electric Ships Based on LSTM Neural Network
2020
Polish Maritime Research
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This paper presents a neural network model of battery SOC estimation, using a long short-term memory (LSTM) recurrent neural network (RNN) as a method for accurate estimation of the SOC in lithium-ion ...
The influence of different numbers of neurons in the neural network's hidden layer on the estimation error is analysed, and the estimation error of the neural network under different training times is ...
ACKNOWLEDGEMENT This work was partially supported by Shanghai S&T Commission under Grant 19040501700 and the Sino-Polish S&T Cooperation Project 37-11. ...
doi:10.2478/pomr-2020-0051
fatcat:7jr6p52injcblnohlpqpyhn5la
XGBoost-Based Remaining Useful Life Estimation Model with Extended Kalman Particle Filter for Lithium-Ion Batteries
2022
Sensors
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Many strategies have been developed to determine the prediction of the RUL and SOH of lithium-ion batteries, including particle filters (PFs). ...
This paper develops a novel PF-based technique for lithium-ion battery RUL estimation, combining a Kalman filter (KF) with a PF to analyze battery operating data. ...
Recurrent neural networks (RNNs) and some of their offshoots, including long short-term memory, have excelled at several sequential tasks. ...
doi:10.3390/s22239522
pmid:36502223
pmcid:PMC9736930
fatcat:hupgzenbajf7dlqdfkcgv2xbsa
Online State of Health Estimation of Lithium-Ion Batteries Based on Charging Process and Long Short-Term Memory Recurrent Neural Network
2023
Batteries
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This paper introduces a new approach to accurately estimate the SOH for rechargeable lithium-ion batteries based on the corresponding charging process and long short-term memory recurrent neural network ...
Accurate state of health (SOH) estimation is critical to the operation, maintenance, and replacement of lithium-ion batteries (LIBs), which have penetrated almost every aspect of our life. ...
In addition, the experiment results show that the LSTM-RNN produces more accurate lithium-ion battery SOH estimates than the gated recurrent unit recurrent neural network (GRU-RNN) and simple recurrent ...
doi:10.3390/batteries9020094
fatcat:gkxz44gu5bfjxloqklhyiyejly
Comparing Single and Hybrid methods of Deep Learning for Remaining Useful Life Prediction of Lithium-ion Batteries
2021
E3S Web of Conferences
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We suggest a hybrid method, named the CNN-LSTM, which is a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM), for predicting and improving the accuracy of the remaining ...
useful life (RUL) of Lithium-ion battery. ...
., Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM) to predict the remaining useful life (RUL) and improve the prediction of the Li-ion batteries. ...
doi:10.1051/e3sconf/202129701043
fatcat:jtd3jh7ydvelrkv63ig2vqdlx4
Ensemble Model Based on Stacked Long Short-Term Memory Model for Cycle Life Prediction of Lithium–Ion Batteries
2020
Applied Sciences
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We present an ensemble model based on the stacked long short-term memory (SLSTM), which is used to predict the capacity cycle life of lithium–ion batteries. ...
To meet the target value of cycle life, it is necessary to accurately assess the lithium–ion capacity degradation in the battery management system. ...
Conflicts of Interest: The authors declare no conflicts of interest. ...
doi:10.3390/app10103549
fatcat:olmkiz2cknctxeiicpwsr6w5bm
Machine Learning-based Lithium-ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles
2019
IEEE Access
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INDEX TERMS Lithium-ion battery, neural network, remaining useful life, capacity estimation, state of health. ...
Specifically, to estimate the state of health accurately we apply feedforward neural network, convolutional neural network, and long short-term memory. ...
LONG SHORT-TERM MEMORY (LSTM) Recurrent neural network (RNN) is a neural network involving directed cycles in memory and shows outstanding performance especially for sequential data. ...
doi:10.1109/access.2019.2920932
fatcat:pasr6z3kovh7phxoixjhcak3tu
LSTM-based Battery Remaining Useful Life Prediction with Multi-Channel Charging Profiles
2020
IEEE Access
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In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). ...
INDEX TERMS Lithium-ion battery, long short-term memory, remaining useful life, capacity estimation. 20786 This work is licensed under a Creative Commons Attribution 4.0 License. ...
[14] used long short-term memory (LSTM) to reflect long-term memories of battery degradation tendency. In addition, combining LSTM and other model is proposed for RUL prediction [15] . ...
doi:10.1109/access.2020.2968939
fatcat:gailoal7yvft7l5logf3kq4lse
State-of-Health Prediction of Lithium-Ion Batteries Based on CNN-BiLSTM-AM
2022
Batteries
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In order to guarantee the safe operation of lithium-ion batteries, a hybrid model based on convolutional neural network (CNN)-bidirectional long short-term memory (BiLSTM) and attention mechanism (AM) ...
all less than 0.01, and can accurately predict the SOH of lithium-ion batteries. ...
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/batteries8100155
fatcat:bohqiin4lfejdppirt3cql7ez4
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