State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model

This paper presents a type of noise-adaptive (NA) interacting multiple model (IMM) algorithm combined with an unscented Kalman filter (UKF) in order to address problems in poor filtering accuracy and filtering divergence of IMM caused by the statistical properties of noise. These properties further...

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Autores principales:	Ce Huang, Xiaoyang Yu, Yongchao Wang, Yongqin Zhou, Ran Li
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	SOC estimation Interacting multiple model Noise adaptive Unscented Kalman filter Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/c452c9c3d1bc41ccbd19bdf5ea74b950
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spelling	oai:doaj.org-article:c452c9c3d1bc41ccbd19bdf5ea74b9502021-11-28T04:34:13ZState of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model2352-484710.1016/j.egyr.2021.09.002https://doaj.org/article/c452c9c3d1bc41ccbd19bdf5ea74b9502021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2352484721008076https://doaj.org/toc/2352-4847This paper presents a type of noise-adaptive (NA) interacting multiple model (IMM) algorithm combined with an unscented Kalman filter (UKF) in order to address problems in poor filtering accuracy and filtering divergence of IMM caused by the statistical properties of noise. These properties further affect the estimation accuracy of state of charge (SOC) when IMM deals with dynamic changes in battery model parameters. Accordingly, the proposed algorithm can realize the accurate estimation of SOC when model parameters change dynamically and when the statistical properties of noise are unknown. By integrating a Sage-Husa noise estimator, NA-IMM-UKF enabled the whole UKF model set to estimate and correct noise information in real time in order for posteriori and unknown noise information to be adjusted adaptively. At the same time, a forgetting factor was introduced in order to optimize the proposed algorithm, thus improving the problem in which the Sage–Husa noise estimator converges slowly when used in conjunction with UKF. By conducting an experiment and simulation, NA-IMM-UKF was shown to carry out SOC estimation under multiple models, with an average error of only 0.4% and maximum error of only 1.08%. However, by comparing the estimated result of SOC under a single model with the Sage–Husa estimator minus the forgetting factor, the average error dropped by 0.15% while the maximum error decreased by 2.78%. In the final noise comparison experiment, following the addition of unknown random noise, the average error of the NA-IMM-UKF algorithm was found to be only 0.48%, while the maximum error was only 1.51%, far surpassing the estimation results of the IMM-UKF algorithm in the same state. As a result, even if the statistical properties of noise are uncertain, the proposed algorithm can still estimate SOC both accurately and effectively.Ce HuangXiaoyang YuYongchao WangYongqin ZhouRan LiElsevierarticleSOC estimationInteracting multiple modelNoise adaptiveUnscented Kalman filterElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENEnergy Reports, Vol 7, Iss , Pp 8152-8161 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	SOC estimation Interacting multiple model Noise adaptive Unscented Kalman filter Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	SOC estimation Interacting multiple model Noise adaptive Unscented Kalman filter Electrical engineering. Electronics. Nuclear engineering TK1-9971 Ce Huang Xiaoyang Yu Yongchao Wang Yongqin Zhou Ran Li State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
description	This paper presents a type of noise-adaptive (NA) interacting multiple model (IMM) algorithm combined with an unscented Kalman filter (UKF) in order to address problems in poor filtering accuracy and filtering divergence of IMM caused by the statistical properties of noise. These properties further affect the estimation accuracy of state of charge (SOC) when IMM deals with dynamic changes in battery model parameters. Accordingly, the proposed algorithm can realize the accurate estimation of SOC when model parameters change dynamically and when the statistical properties of noise are unknown. By integrating a Sage-Husa noise estimator, NA-IMM-UKF enabled the whole UKF model set to estimate and correct noise information in real time in order for posteriori and unknown noise information to be adjusted adaptively. At the same time, a forgetting factor was introduced in order to optimize the proposed algorithm, thus improving the problem in which the Sage–Husa noise estimator converges slowly when used in conjunction with UKF. By conducting an experiment and simulation, NA-IMM-UKF was shown to carry out SOC estimation under multiple models, with an average error of only 0.4% and maximum error of only 1.08%. However, by comparing the estimated result of SOC under a single model with the Sage–Husa estimator minus the forgetting factor, the average error dropped by 0.15% while the maximum error decreased by 2.78%. In the final noise comparison experiment, following the addition of unknown random noise, the average error of the NA-IMM-UKF algorithm was found to be only 0.48%, while the maximum error was only 1.51%, far surpassing the estimation results of the IMM-UKF algorithm in the same state. As a result, even if the statistical properties of noise are uncertain, the proposed algorithm can still estimate SOC both accurately and effectively.
format	article
author	Ce Huang Xiaoyang Yu Yongchao Wang Yongqin Zhou Ran Li
author_facet	Ce Huang Xiaoyang Yu Yongchao Wang Yongqin Zhou Ran Li
author_sort	Ce Huang
title	State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
title_short	State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
title_full	State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
title_fullStr	State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
title_full_unstemmed	State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
title_sort	state of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/c452c9c3d1bc41ccbd19bdf5ea74b950
work_keys_str_mv	AT cehuang stateofchargeestimationofliionbatteriesbasedonthenoiseadaptiveinteractingmultiplemodel AT xiaoyangyu stateofchargeestimationofliionbatteriesbasedonthenoiseadaptiveinteractingmultiplemodel AT yongchaowang stateofchargeestimationofliionbatteriesbasedonthenoiseadaptiveinteractingmultiplemodel AT yongqinzhou stateofchargeestimationofliionbatteriesbasedonthenoiseadaptiveinteractingmultiplemodel AT ranli stateofchargeestimationofliionbatteriesbasedonthenoiseadaptiveinteractingmultiplemodel
_version_	1718408342912630784

State of charge estimation of li-ion batteries based on the noise-adaptive interacting multiple model

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