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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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 |
work_keys_str_mv |
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