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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/23/9277 |
| _version_ | 1797462126248329216 |
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| author | You Fu Binhao Zhai Zhuoqun Shi Jun Liang Zhouhua Peng |
| author_facet | You Fu Binhao Zhai Zhuoqun Shi Jun Liang Zhouhua Peng |
| author_sort | You Fu |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T17:32:04Z |
| format | Article |
| id | doaj.art-95ef13f10d86407d989936dd93465147 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T17:32:04Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-95ef13f10d86407d989936dd934651472023-11-24T12:11:31ZengMDPI AGSensors1424-82202022-11-012223927710.3390/s22239277State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVsYou Fu0Binhao Zhai1Zhuoqun Shi2Jun Liang3Zhouhua Peng4School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116024, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116024, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116024, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116024, ChinaSchool of Marine Electrical Engineering, Dalian Maritime University, Dalian 116024, ChinaAs 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.https://www.mdpi.com/1424-8220/22/23/9277lithium-ion batterystate of chargeforgetting factor recursive least squaresadaptive iterative extended Kalman filter |
| spellingShingle | You Fu Binhao Zhai Zhuoqun Shi Jun Liang Zhouhua Peng State of Charge Estimation of Lithium-Ion Batteries Based on an Adaptive Iterative Extended Kalman Filter for AUVs Sensors lithium-ion battery state of charge forgetting factor recursive least squares adaptive iterative extended Kalman filter |
| 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 | lithium-ion battery state of charge forgetting factor recursive least squares adaptive iterative extended Kalman filter |
| url | https://www.mdpi.com/1424-8220/22/23/9277 |
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