A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine

Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yanjun Xiao [verfasserIn] Shuhan Deng [verfasserIn] Furong Han [verfasserIn] Xiaoliang Wang [verfasserIn] Zonghua Zhang [verfasserIn] Kai Peng [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Multiform coupling time series forecasting Bayesian LSTM deep fusion predictor

Übergeordnetes Werk:	In: IEEE Access - IEEE, 2014, 10(2022), Seite 55034-55050
Übergeordnetes Werk:	volume:10 ; year:2022 ; pages:55034-55050

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1109/ACCESS.2022.3176900

Katalog-ID:	DOAJ029606470

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allfields	10.1109/ACCESS.2022.3176900 doi (DE-627)DOAJ029606470 (DE-599)DOAJ33f17d5fa2b34fd68e8a93e0781ca9f8 DE-627 ger DE-627 rakwb eng TK1-9971 Yanjun Xiao verfasserin aut A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance. Multiform coupling time series forecasting Bayesian LSTM deep fusion predictor Electrical engineering. Electronics. Nuclear engineering Shuhan Deng verfasserin aut Furong Han verfasserin aut Xiaoliang Wang verfasserin aut Zonghua Zhang verfasserin aut Kai Peng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 55034-55050 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:55034-55050 https://doi.org/10.1109/ACCESS.2022.3176900 kostenfrei https://doaj.org/article/33f17d5fa2b34fd68e8a93e0781ca9f8 kostenfrei https://ieeexplore.ieee.org/document/9779730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 55034-55050
spelling	10.1109/ACCESS.2022.3176900 doi (DE-627)DOAJ029606470 (DE-599)DOAJ33f17d5fa2b34fd68e8a93e0781ca9f8 DE-627 ger DE-627 rakwb eng TK1-9971 Yanjun Xiao verfasserin aut A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance. Multiform coupling time series forecasting Bayesian LSTM deep fusion predictor Electrical engineering. Electronics. Nuclear engineering Shuhan Deng verfasserin aut Furong Han verfasserin aut Xiaoliang Wang verfasserin aut Zonghua Zhang verfasserin aut Kai Peng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 55034-55050 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:55034-55050 https://doi.org/10.1109/ACCESS.2022.3176900 kostenfrei https://doaj.org/article/33f17d5fa2b34fd68e8a93e0781ca9f8 kostenfrei https://ieeexplore.ieee.org/document/9779730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 55034-55050
allfields_unstemmed	10.1109/ACCESS.2022.3176900 doi (DE-627)DOAJ029606470 (DE-599)DOAJ33f17d5fa2b34fd68e8a93e0781ca9f8 DE-627 ger DE-627 rakwb eng TK1-9971 Yanjun Xiao verfasserin aut A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance. Multiform coupling time series forecasting Bayesian LSTM deep fusion predictor Electrical engineering. Electronics. Nuclear engineering Shuhan Deng verfasserin aut Furong Han verfasserin aut Xiaoliang Wang verfasserin aut Zonghua Zhang verfasserin aut Kai Peng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 55034-55050 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:55034-55050 https://doi.org/10.1109/ACCESS.2022.3176900 kostenfrei https://doaj.org/article/33f17d5fa2b34fd68e8a93e0781ca9f8 kostenfrei https://ieeexplore.ieee.org/document/9779730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 55034-55050
allfieldsGer	10.1109/ACCESS.2022.3176900 doi (DE-627)DOAJ029606470 (DE-599)DOAJ33f17d5fa2b34fd68e8a93e0781ca9f8 DE-627 ger DE-627 rakwb eng TK1-9971 Yanjun Xiao verfasserin aut A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance. Multiform coupling time series forecasting Bayesian LSTM deep fusion predictor Electrical engineering. Electronics. Nuclear engineering Shuhan Deng verfasserin aut Furong Han verfasserin aut Xiaoliang Wang verfasserin aut Zonghua Zhang verfasserin aut Kai Peng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 55034-55050 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:55034-55050 https://doi.org/10.1109/ACCESS.2022.3176900 kostenfrei https://doaj.org/article/33f17d5fa2b34fd68e8a93e0781ca9f8 kostenfrei https://ieeexplore.ieee.org/document/9779730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 55034-55050
allfieldsSound	10.1109/ACCESS.2022.3176900 doi (DE-627)DOAJ029606470 (DE-599)DOAJ33f17d5fa2b34fd68e8a93e0781ca9f8 DE-627 ger DE-627 rakwb eng TK1-9971 Yanjun Xiao verfasserin aut A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance. Multiform coupling time series forecasting Bayesian LSTM deep fusion predictor Electrical engineering. Electronics. Nuclear engineering Shuhan Deng verfasserin aut Furong Han verfasserin aut Xiaoliang Wang verfasserin aut Zonghua Zhang verfasserin aut Kai Peng verfasserin aut In IEEE Access IEEE, 2014 10(2022), Seite 55034-55050 (DE-627)728440385 (DE-600)2687964-5 21693536 nnns volume:10 year:2022 pages:55034-55050 https://doi.org/10.1109/ACCESS.2022.3176900 kostenfrei https://doaj.org/article/33f17d5fa2b34fd68e8a93e0781ca9f8 kostenfrei https://ieeexplore.ieee.org/document/9779730/ kostenfrei https://doaj.org/toc/2169-3536 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 10 2022 55034-55050
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This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. 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callnumber-first	T - Technology
author	Yanjun Xiao
spellingShingle	Yanjun Xiao misc TK1-9971 misc Multiform coupling misc time series forecasting misc Bayesian LSTM misc deep fusion predictor misc Electrical engineering. Electronics. Nuclear engineering A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine
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topic_title	TK1-9971 A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine Multiform coupling time series forecasting Bayesian LSTM deep fusion predictor
topic	misc TK1-9971 misc Multiform coupling misc time series forecasting misc Bayesian LSTM misc deep fusion predictor misc Electrical engineering. Electronics. Nuclear engineering
topic_unstemmed	misc TK1-9971 misc Multiform coupling misc time series forecasting misc Bayesian LSTM misc deep fusion predictor misc Electrical engineering. Electronics. Nuclear engineering
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title	A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine
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title_full	A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine
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title_sort	model-data-fusion pole piece thickness prediction method with multisensor fusion for lithium battery rolling machine
callnumber	TK1-9971
title_auth	A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine
abstract	Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance.
abstractGer	Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance.
abstract_unstemmed	Trend prediction based on sensor data is an important topic in the thickness control system of lithium battery electrode mills. As the number of sensors increases, we can measure and store more and more data. The characteristics of nonlinearity, uncertainty, and time-variability in the lithium battery electrode thickness control system. The increase of control system complexity and data volume does not effectively improve the prediction performance. This paper proposes a physical-data fusion modeling prediction method based on a multiform coupling model and Bayesian LSTM (Bayesian Long Short-Term Memory) to achieve dynamic prediction of lithium battery electrode thickness, overcome data irrelevance and sensor noise, ensure the consistency of electrode thickness, and improve the operational efficiency of battery electrode production: Firstly, we establish the underlying physical model of the roll to further obtain the specific parameters affecting the thickness control and overcome the data irrelevance and sensor noise; secondly, we use Bayesian method to obtain the characteristics of the weight distribution of the sub-prediction network and construct the Bayesian LSTM predictor. An MLP (Multilayer Perceptron) is used as the fusion layer to fuse the results of different sub-predictions to improve the robustness of the nonlinear control system prediction model and solve the problems of slow approximation speed and ease to fall into local minimization of traditional neural networks. Finally, the advantages of the deep learning model are analyzed in terms of data feature self-extraction and model generalization generalizability. Compared with other neural network models, Bayesian LSTM has better generalizability for small sample data. The results show that the predictor can effectively model the large measurement data of the thickness control system of lithium battery electrode mills and improve the prediction performance.
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title_short	A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine
url	https://doi.org/10.1109/ACCESS.2022.3176900 https://doaj.org/article/33f17d5fa2b34fd68e8a93e0781ca9f8 https://ieeexplore.ieee.org/document/9779730/ https://doaj.org/toc/2169-3536
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author2	Shuhan Deng Furong Han Xiaoliang Wang Zonghua Zhang Kai Peng
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doi_str	10.1109/ACCESS.2022.3176900
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up_date	2024-07-03T23:38:24.287Z
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A Model-Data-Fusion Pole Piece Thickness Prediction Method With Multisensor Fusion for Lithium Battery Rolling Machine

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