Neural network-based Li-Ion battery aging model at accelerated C-Rate

Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging...

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Main Authors: Hoque, Md Azizul, Hassan, Mohd Khair, Hajjo, Abdulrahman, Tokhi, Mohammad Osman
Format: Article
Published: Multidisciplinary Digital Publishing Institute 2023
Online Access:http://psasir.upm.edu.my/id/eprint/109243/
https://www.mdpi.com/2313-0105/9/2/93
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Institution: Universiti Putra Malaysia
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spelling my.upm.eprints.1092432024-08-20T06:37:20Z http://psasir.upm.edu.my/id/eprint/109243/ Neural network-based Li-Ion battery aging model at accelerated C-Rate Hoque, Md Azizul Hassan, Mohd Khair Hajjo, Abdulrahman Tokhi, Mohammad Osman Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in discharge. Monitoring the battery cycle life at various discharge rates would enable the battery management system (BMS) to implement control parameters to resolve the aging issue. In this paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate). Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network (RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of the developed models is carried out and it is shown that the LSTM-RNN battery aging model has superior performance at accelerated C-rate compared to the traditional FNN network. Multidisciplinary Digital Publishing Institute 2023-01-29 Article PeerReviewed Hoque, Md Azizul and Hassan, Mohd Khair and Hajjo, Abdulrahman and Tokhi, Mohammad Osman (2023) Neural network-based Li-Ion battery aging model at accelerated C-Rate. Batteries, 9 (2). art. no. 93. pp. 1-17. ISSN 2313-0105 https://www.mdpi.com/2313-0105/9/2/93 10.3390/batteries9020093
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description Lithium-ion (Li-ion) batteries are widely used in electric vehicles (EVs) because of their high energy density, low self-discharge, and superior performance. Despite this, Li-ion batteries’ performance and reliability become critical as they lose their capacity with increasing charge and discharging cycles. Moreover, Li-ion batteries are subject to aging in EVs due to load variations in discharge. Monitoring the battery cycle life at various discharge rates would enable the battery management system (BMS) to implement control parameters to resolve the aging issue. In this paper, a battery lifetime degradation model is proposed at an accelerated current rate (C-rate). Furthermore, an ideal lifetime discharge rate within the standard C-rate and beyond the C-rate is proposed. The consequence of discharging at an accelerated C-rate on the cycle life of the batteries is thoroughly investigated. Moreover, the battery degradation model is investigated with a deep learning algorithm-based feed-forward neural network (FNN), and a recurrent neural network (RNN) with long short-term memory (LSTM) layer. A comparative assessment of performance of the developed models is carried out and it is shown that the LSTM-RNN battery aging model has superior performance at accelerated C-rate compared to the traditional FNN network.
format Article
author Hoque, Md Azizul
Hassan, Mohd Khair
Hajjo, Abdulrahman
Tokhi, Mohammad Osman
spellingShingle Hoque, Md Azizul
Hassan, Mohd Khair
Hajjo, Abdulrahman
Tokhi, Mohammad Osman
Neural network-based Li-Ion battery aging model at accelerated C-Rate
author_facet Hoque, Md Azizul
Hassan, Mohd Khair
Hajjo, Abdulrahman
Tokhi, Mohammad Osman
author_sort Hoque, Md Azizul
title Neural network-based Li-Ion battery aging model at accelerated C-Rate
title_short Neural network-based Li-Ion battery aging model at accelerated C-Rate
title_full Neural network-based Li-Ion battery aging model at accelerated C-Rate
title_fullStr Neural network-based Li-Ion battery aging model at accelerated C-Rate
title_full_unstemmed Neural network-based Li-Ion battery aging model at accelerated C-Rate
title_sort neural network-based li-ion battery aging model at accelerated c-rate
publisher Multidisciplinary Digital Publishing Institute
publishDate 2023
url http://psasir.upm.edu.my/id/eprint/109243/
https://www.mdpi.com/2313-0105/9/2/93
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