Cargando…
State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs
As a power source for autonomous underwater vehicles (AUVs), lithium-ion batteries play an important role in ensuring AUVs’ electric power propulsion performance. An accurate state of charge (SOC) estimation method is the key to achieving energy optimization for lithium-ion batteries. Due to the com...
Autores principales: | , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741131/ https://www.ncbi.nlm.nih.gov/pubmed/36501979 http://dx.doi.org/10.3390/s22239277 |
_version_ | 1784848241549901824 |
---|---|
author | Fu, You Zhai, Binhao Shi, Zhuoqun Liang, Jun Peng, Zhouhua |
author_facet | Fu, You Zhai, Binhao Shi, Zhuoqun Liang, Jun Peng, Zhouhua |
author_sort | Fu, You |
collection | PubMed |
description | As a power source for autonomous underwater vehicles (AUVs), lithium-ion batteries play an important role in ensuring AUVs’ electric power propulsion performance. An accurate state of charge (SOC) estimation method is the key to achieving energy optimization for lithium-ion batteries. Due to the complicated ocean environments, traditional filtering methods cannot effectively estimate the SOC of lithium-ion batteries in an AUV. Based on the standard extended Kalman filter (EKF), an adaptive iterative extended Kalman filter (AIEKF) method for the SOC in an AUV is proposed to address the traditional filter’s problems, such as low accuracy and large errors. In this method, the adaptive update is introduced to deal with the uncertain noise from the lithium-ion battery. The iteration is used to improve the convergence speed and to reduce the computational burden. Compared with the EKF, iterative extended Kalman filter (IEKF) and adaptive extended Kalman filter (AEKF), the proposed AIEKF has a higher estimation accuracy and anti-interference capability, which is suitable for the AUV’s SOC estimation. In addition, based on the second-order equivalent circuit model of the lithium-ion battery, a forgetting factor recursive least squares (FFRLS) method is proposed to deal with the multi-variability problem. In the end, four different methods, including EKF, IEKF, AEKF, and the proposed AIEKF, are compared in computational time. The experiment results show that the proposed method has high accuracy and fast estimation speed, meaning that it has good application potential in AUVs. |
format | Online Article Text |
id | pubmed-9741131 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97411312022-12-11 State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs Fu, You Zhai, Binhao Shi, Zhuoqun Liang, Jun Peng, Zhouhua Sensors (Basel) Article As a power source for autonomous underwater vehicles (AUVs), lithium-ion batteries play an important role in ensuring AUVs’ electric power propulsion performance. An accurate state of charge (SOC) estimation method is the key to achieving energy optimization for lithium-ion batteries. Due to the complicated ocean environments, traditional filtering methods cannot effectively estimate the SOC of lithium-ion batteries in an AUV. Based on the standard extended Kalman filter (EKF), an adaptive iterative extended Kalman filter (AIEKF) method for the SOC in an AUV is proposed to address the traditional filter’s problems, such as low accuracy and large errors. In this method, the adaptive update is introduced to deal with the uncertain noise from the lithium-ion battery. The iteration is used to improve the convergence speed and to reduce the computational burden. Compared with the EKF, iterative extended Kalman filter (IEKF) and adaptive extended Kalman filter (AEKF), the proposed AIEKF has a higher estimation accuracy and anti-interference capability, which is suitable for the AUV’s SOC estimation. In addition, based on the second-order equivalent circuit model of the lithium-ion battery, a forgetting factor recursive least squares (FFRLS) method is proposed to deal with the multi-variability problem. In the end, four different methods, including EKF, IEKF, AEKF, and the proposed AIEKF, are compared in computational time. The experiment results show that the proposed method has high accuracy and fast estimation speed, meaning that it has good application potential in AUVs. MDPI 2022-11-29 /pmc/articles/PMC9741131/ /pubmed/36501979 http://dx.doi.org/10.3390/s22239277 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Fu, You Zhai, Binhao Shi, Zhuoqun Liang, Jun Peng, Zhouhua State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs |
title | State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs |
title_full | State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs |
title_fullStr | State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs |
title_full_unstemmed | State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs |
title_short | State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs |
title_sort | state of charge estimation of lithium-ion batteries based on an adaptive iterative extended kalman filter for auvs |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9741131/ https://www.ncbi.nlm.nih.gov/pubmed/36501979 http://dx.doi.org/10.3390/s22239277 |
work_keys_str_mv | AT fuyou stateofchargeestimationoflithiumionbatteriesbasedonanadaptiveiterativeextendedkalmanfilterforauvs AT zhaibinhao stateofchargeestimationoflithiumionbatteriesbasedonanadaptiveiterativeextendedkalmanfilterforauvs AT shizhuoqun stateofchargeestimationoflithiumionbatteriesbasedonanadaptiveiterativeextendedkalmanfilterforauvs AT liangjun stateofchargeestimationoflithiumionbatteriesbasedonanadaptiveiterativeextendedkalmanfilterforauvs AT pengzhouhua stateofchargeestimationoflithiumionbatteriesbasedonanadaptiveiterativeextendedkalmanfilterforauvs |