Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasi...

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Autores principales:	Sungwoo Jo, Sunkyu Jung, Taemoon Roh
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	data preprocessing data-driven approaches lithium-ion battery neural network state of charge SOH estimation Technology T
Acceso en línea:	https://doaj.org/article/85d0be4c45ce4c6ba53a4f5fc0b08fa5
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spelling	oai:doaj.org-article:85d0be4c45ce4c6ba53a4f5fc0b08fa52021-11-11T15:58:41ZBattery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge10.3390/en142172061996-1073https://doaj.org/article/85d0be4c45ce4c6ba53a4f5fc0b08fa52021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7206https://doaj.org/toc/1996-1073Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.Sungwoo JoSunkyu JungTaemoon RohMDPI AGarticledata preprocessingdata-driven approacheslithium-ion batteryneural networkstate of chargeSOH estimationTechnologyTENEnergies, Vol 14, Iss 7206, p 7206 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data preprocessing data-driven approaches lithium-ion battery neural network state of charge SOH estimation Technology T
spellingShingle	data preprocessing data-driven approaches lithium-ion battery neural network state of charge SOH estimation Technology T Sungwoo Jo Sunkyu Jung Taemoon Roh Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
description	Because lithium-ion batteries are widely used for various purposes, it is important to estimate their state of health (SOH) to ensure their efficiency and safety. Despite the usefulness of model-based methods for SOH estimation, the difficulties of battery modeling have resulted in a greater emphasis on machine learning for SOH estimation. Furthermore, data preprocessing has received much attention because it is an important step in determining the efficiency of machine learning methods. In this paper, we propose a new preprocessing method for improving the efficiency of machine learning for SOH estimation. The proposed method consists of the relative state of charge (SOC) and data processing, which transforms time-domain data into SOC-domain data. According to the correlation analysis, SOC-domain data are more correlated with the usable capacity than time-domain data. Furthermore, we compare the estimation results of SOC-based data and time-based data in feedforward neural networks (FNNs), convolutional neural networks (CNNs), and long short-term memory (LSTM). The results show that the SOC-based preprocessing outperforms conventional time-domain data-based techniques. Furthermore, the accuracy of the simplest FNN model with the proposed method is higher than that of the CNN model and the LSTM model with a conventional method when training data are small.
format	article
author	Sungwoo Jo Sunkyu Jung Taemoon Roh
author_facet	Sungwoo Jo Sunkyu Jung Taemoon Roh
author_sort	Sungwoo Jo
title	Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
title_short	Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
title_full	Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
title_fullStr	Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
title_full_unstemmed	Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge
title_sort	battery state-of-health estimation using machine learning and preprocessing with relative state-of-charge
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/85d0be4c45ce4c6ba53a4f5fc0b08fa5
work_keys_str_mv	AT sungwoojo batterystateofhealthestimationusingmachinelearningandpreprocessingwithrelativestateofcharge AT sunkyujung batterystateofhealthestimationusingmachinelearningandpreprocessingwithrelativestateofcharge AT taemoonroh batterystateofhealthestimationusingmachinelearningandpreprocessingwithrelativestateofcharge
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Battery State-of-Health Estimation Using Machine Learning and Preprocessing with Relative State-of-Charge

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