Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper...

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Autores principales:	Shaheer Ansari, Afida Ayob, Molla Shahadat Hossain Lipu, Aini Hussain, Mohamad Hanif Md Saad
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	lithium-ion battery remaining useful life electric vehicles backpropagation neural network multi-channel input (MCI) profile Technology T
Acceso en línea:	https://doaj.org/article/8773643e040a41459c1978729eacd85c
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spelling	oai:doaj.org-article:8773643e040a41459c1978729eacd85c2021-11-25T17:26:15ZMulti-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries10.3390/en142275211996-1073https://doaj.org/article/8773643e040a41459c1978729eacd85c2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7521https://doaj.org/toc/1996-1073Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.Shaheer AnsariAfida AyobMolla Shahadat Hossain LipuAini HussainMohamad Hanif Md SaadMDPI AGarticlelithium-ion batteryremaining useful lifeelectric vehiclesbackpropagation neural networkmulti-channel input (MCI) profileTechnologyTENEnergies, Vol 14, Iss 7521, p 7521 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	lithium-ion battery remaining useful life electric vehicles backpropagation neural network multi-channel input (MCI) profile Technology T
spellingShingle	lithium-ion battery remaining useful life electric vehicles backpropagation neural network multi-channel input (MCI) profile Technology T Shaheer Ansari Afida Ayob Molla Shahadat Hossain Lipu Aini Hussain Mohamad Hanif Md Saad Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries
description	Remaining useful life (RUL) is a crucial assessment indicator to evaluate battery efficiency, robustness, and accuracy by determining battery failure occurrence in electric vehicle (EV) applications. RUL prediction is necessary for timely maintenance and replacement of the battery in EVs. This paper proposes an artificial neural network (ANN) technique to predict the RUL of lithium-ion batteries under various training datasets. A multi-channel input (MCI) profile is implemented and compared with single-channel input (SCI) or single input (SI) with diverse datasets. A NASA battery dataset is utilized and systematic sampling is implemented to extract 10 sample values of voltage, current, and temperature at equal intervals from each charging cycle to reconstitute the input training profile. The experimental results demonstrate that MCI profile-based RUL prediction is highly accurate compared to SCI profile under diverse datasets. It is reported that RMSE for the proposed MCI profile-based ANN technique is 0.0819 compared to 0.5130 with SCI profile for the B0005 battery dataset. Moreover, RMSE is higher when the proposed model is trained with two datasets and one dataset, respectively. Additionally, the importance of capacity regeneration phenomena in batteries B0006 and B0018 to predict battery RUL is investigated. The results demonstrate that RMSE for the testing battery dataset B0005 is 3.7092, 3.9373 when trained with B0006, B0018, respectively, while it is 3.3678 when trained with B0007 due to the effect of capacity regeneration in B0006 and B0018 battery datasets.
format	article
author	Shaheer Ansari Afida Ayob Molla Shahadat Hossain Lipu Aini Hussain Mohamad Hanif Md Saad
author_facet	Shaheer Ansari Afida Ayob Molla Shahadat Hossain Lipu Aini Hussain Mohamad Hanif Md Saad
author_sort	Shaheer Ansari
title	Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries
title_short	Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries
title_full	Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries
title_fullStr	Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries
title_full_unstemmed	Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries
title_sort	multi-channel profile based artificial neural network approach for remaining useful life prediction of electric vehicle lithium-ion batteries
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8773643e040a41459c1978729eacd85c
work_keys_str_mv	AT shaheeransari multichannelprofilebasedartificialneuralnetworkapproachforremainingusefullifepredictionofelectricvehiclelithiumionbatteries AT afidaayob multichannelprofilebasedartificialneuralnetworkapproachforremainingusefullifepredictionofelectricvehiclelithiumionbatteries AT mollashahadathossainlipu multichannelprofilebasedartificialneuralnetworkapproachforremainingusefullifepredictionofelectricvehiclelithiumionbatteries AT ainihussain multichannelprofilebasedartificialneuralnetworkapproachforremainingusefullifepredictionofelectricvehiclelithiumionbatteries AT mohamadhanifmdsaad multichannelprofilebasedartificialneuralnetworkapproachforremainingusefullifepredictionofelectricvehiclelithiumionbatteries
_version_	1718412360084881408

Multi-Channel Profile Based Artificial Neural Network Approach for Remaining Useful Life Prediction of Electric Vehicle Lithium-Ion Batteries

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