H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery

Accurate state of charge (SoC) estimation is crucial for the safe and reliable running of lithium-ion batteries in electrified transportation equipment. To enhance the estimation accuracy and robustness under different ambient temperatures, H∞ and the adaptive H∞ filterings were first combined to si...

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Main Authors: Hailong Feng, Zhifu Wang, Fujun Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-09-01
Series:Frontiers in Energy Research
Subjects:
Online Access:https://www.frontiersin.org/articles/10.3389/fenrg.2021.717722/full
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spelling doaj-ab73f36643204de9a1459a5a4bd014a52021-09-20T04:52:48ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2021-09-01910.3389/fenrg.2021.717722717722H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer BatteryHailong FengZhifu WangFujun ZhangAccurate state of charge (SoC) estimation is crucial for the safe and reliable running of lithium-ion batteries in electrified transportation equipment. To enhance the estimation accuracy and robustness under different ambient temperatures, H∞ and the adaptive H∞ filterings were first combined to simultaneously forecast the parameters and SoC of the battery model considering the hysteresis effect in this paper. To drop the computational complexity to the most extent, the hysteresis unit was integrated into the first-order RC battery model and the aforementioned combined algorithm was developed under a dual-time frame. Then, the battery model with the hysteresis effect is evaluated against the model without that in terms of the estimation accuracy. Subsequently, the proposed algorithm is compared with the dual H∞ algorithm based on the employed battery model. The results demonstrate the excellent performance of the utilized battery model and the proposed algorithm in terms of both the estimation accuracy and the convergence speed.https://www.frontiersin.org/articles/10.3389/fenrg.2021.717722/fullH∞ filteradaptive H∞ filterlithium-ion batteriesstate of charge (SoC)hysteresis
collection DOAJ
language English
format Article
sources DOAJ
author Hailong Feng
Zhifu Wang
Fujun Zhang
spellingShingle Hailong Feng
Zhifu Wang
Fujun Zhang
H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery
Frontiers in Energy Research
H∞ filter
adaptive H∞ filter
lithium-ion batteries
state of charge (SoC)
hysteresis
author_facet Hailong Feng
Zhifu Wang
Fujun Zhang
author_sort Hailong Feng
title H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery
title_short H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery
title_full H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery
title_fullStr H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery
title_full_unstemmed H∞–Adaptive H∞ Algorithm-Based State of Charge Estimation Considering the Hysteresis Effect for Lithium Polymer Battery
title_sort h∞–adaptive h∞ algorithm-based state of charge estimation considering the hysteresis effect for lithium polymer battery
publisher Frontiers Media S.A.
series Frontiers in Energy Research
issn 2296-598X
publishDate 2021-09-01
description Accurate state of charge (SoC) estimation is crucial for the safe and reliable running of lithium-ion batteries in electrified transportation equipment. To enhance the estimation accuracy and robustness under different ambient temperatures, H∞ and the adaptive H∞ filterings were first combined to simultaneously forecast the parameters and SoC of the battery model considering the hysteresis effect in this paper. To drop the computational complexity to the most extent, the hysteresis unit was integrated into the first-order RC battery model and the aforementioned combined algorithm was developed under a dual-time frame. Then, the battery model with the hysteresis effect is evaluated against the model without that in terms of the estimation accuracy. Subsequently, the proposed algorithm is compared with the dual H∞ algorithm based on the employed battery model. The results demonstrate the excellent performance of the utilized battery model and the proposed algorithm in terms of both the estimation accuracy and the convergence speed.
topic H∞ filter
adaptive H∞ filter
lithium-ion batteries
state of charge (SoC)
hysteresis
url https://www.frontiersin.org/articles/10.3389/fenrg.2021.717722/full
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