The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries

It is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of bat...

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Main Authors: Xin Sui, Daniel-Ioan Stroe, Shan He, Xinrong Huang, Jinhao Meng, Remus Teodorescu
Format: Article
Language:English
Published: MDPI AG 2019-10-01
Series:Applied Sciences
Subjects:
lithium-ion battery
capacity estimation
entropy
current pulse
Online Access:https://www.mdpi.com/2076-3417/9/19/4170
id doaj-8b0b2acf20734f598124e8f6e22bdb84
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spelling doaj-8b0b2acf20734f598124e8f6e22bdb842020-11-25T02:03:28ZengMDPI AGApplied Sciences2076-34172019-10-01919417010.3390/app9194170app9194170The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion BatteriesXin Sui0Daniel-Ioan Stroe1Shan He2Xinrong Huang3Jinhao Meng4Remus Teodorescu5Department of Energy Technology, Aalborg University, 9220 Aalborg, DenmarkDepartment of Energy Technology, Aalborg University, 9220 Aalborg, DenmarkDepartment of Energy Technology, Aalborg University, 9220 Aalborg, DenmarkDepartment of Energy Technology, Aalborg University, 9220 Aalborg, DenmarkSchool of Automation, Northwestern Polytechnical University, Xi’an 710072, ChinaDepartment of Energy Technology, Aalborg University, 9220 Aalborg, DenmarkIt is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of batteries. In order to analyze the effect of voltage dataset selection on the accuracy of entropy-based estimation methods, six voltage datasets were collected, considering the current direction (i.e., charging or discharging) and the state of charge level. Furthermore, three kinds of entropies (approximate entropy, sample entropy, and multiscale entropy) were introduced, and the relationship between the entropies and the battery capacity was established by using first-order polynomial fitting. Finally, the interaction between the test conditions, entropy features, and estimation accuracy was analyzed. Moreover, the results can be used to select the correct voltage dataset and improve the estimation accuracy.https://www.mdpi.com/2076-3417/9/19/4170lithium-ion batterycapacity estimationentropycurrent pulse
collection DOAJ
language English
format Article
sources DOAJ
author Xin Sui
Daniel-Ioan Stroe
Shan He
Xinrong Huang
Jinhao Meng
Remus Teodorescu
spellingShingle Xin Sui
Daniel-Ioan Stroe
Shan He
Xinrong Huang
Jinhao Meng
Remus Teodorescu
The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries
Applied Sciences
lithium-ion battery
capacity estimation
entropy
current pulse
author_facet Xin Sui
Daniel-Ioan Stroe
Shan He
Xinrong Huang
Jinhao Meng
Remus Teodorescu
author_sort Xin Sui
title The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries
title_short The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries
title_full The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries
title_fullStr The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries
title_full_unstemmed The Effect of Voltage Dataset Selection on the Accuracy of Entropy-Based Capacity Estimation Methods for Lithium-Ion Batteries
title_sort effect of voltage dataset selection on the accuracy of entropy-based capacity estimation methods for lithium-ion batteries
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2019-10-01
description It is important to accurately estimate the capacity of the battery in order to extend the service life of the battery and ensure the reliable operation of the battery energy storage system. As entropy can quantify the regularity of a dataset, it can serve as a feature to estimate the capacity of batteries. In order to analyze the effect of voltage dataset selection on the accuracy of entropy-based estimation methods, six voltage datasets were collected, considering the current direction (i.e., charging or discharging) and the state of charge level. Furthermore, three kinds of entropies (approximate entropy, sample entropy, and multiscale entropy) were introduced, and the relationship between the entropies and the battery capacity was established by using first-order polynomial fitting. Finally, the interaction between the test conditions, entropy features, and estimation accuracy was analyzed. Moreover, the results can be used to select the correct voltage dataset and improve the estimation accuracy.
topic lithium-ion battery
capacity estimation
entropy
current pulse
url https://www.mdpi.com/2076-3417/9/19/4170
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