Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method
The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is fu...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/88313 http://hdl.handle.net/10220/45668 |
| _version_ | 1811688742538182656 |
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| author | Wei, Zhongbao Leng, Feng He, Zhongjie Zhang, Wenyu Li, Kaiyuan |
| author2 | Energy Research Institute @ NTU (ERI@N) |
| author_facet | Energy Research Institute @ NTU (ERI@N) Wei, Zhongbao Leng, Feng He, Zhongjie Zhang, Wenyu Li, Kaiyuan |
| author_sort | Wei, Zhongbao |
| collection | NTU |
| description | The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation’s alertness and numerical stability so as to achieve an accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data–model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy. |
| first_indexed | 2024-10-01T05:37:03Z |
| format | Journal Article |
| id | ntu-10356/88313 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:37:03Z |
| publishDate | 2018 |
| record_format | dspace |
| spelling | ntu-10356/883132021-01-13T02:32:39Z Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method Wei, Zhongbao Leng, Feng He, Zhongjie Zhang, Wenyu Li, Kaiyuan Energy Research Institute @ NTU (ERI@N) State of Health State of Charge DRNTU::Engineering::Electrical and electronic engineering The accurate monitoring of state of charge (SOC) and state of health (SOH) is critical for the reliable management of lithium-ion battery (LIB) systems. In this paper, online model identification is scrutinized to realize high modeling accuracy and robustness, and a model-based joint estimator is further proposed to estimate the SOC and SOH of an LIB concurrently. Specifically, an adaptive forgetting recursive least squares (AF-RLS) method is exploited to optimize the estimation’s alertness and numerical stability so as to achieve an accurate online adaption of model parameters. Leveraging the online adapted battery model, a joint estimator is proposed by combining an open-circuit voltage (OCV) observer with a low-order state observer to co-estimate the SOC and capacity of an LIB. Simulation and experimental studies are performed to verify the feasibility of the proposed data–model fusion method. The proposed method is shown to effectively track the variation of model parameters by using the onboard measured current and voltage data. The SOC and capacity can be further estimated in real time with fast convergence, high stability, and high accuracy. Published version 2018-08-23T08:10:00Z 2019-12-06T17:00:28Z 2018-08-23T08:10:00Z 2019-12-06T17:00:28Z 2018 Journal Article Wei, Z., Leng, F., He, Z., Zhang, W., & Li, K. (2018). Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method. Energies, 11(7), 1810-. doi:10.3390/en11071810 1996-1073 https://hdl.handle.net/10356/88313 http://hdl.handle.net/10220/45668 10.3390/en11071810 en Energies © 2018 The Author(s). 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 (http://creativecommons.org/licenses/by/4.0/) 16 p. application/pdf |
| spellingShingle | State of Health State of Charge DRNTU::Engineering::Electrical and electronic engineering Wei, Zhongbao Leng, Feng He, Zhongjie Zhang, Wenyu Li, Kaiyuan Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method |
| title | Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method |
| title_full | Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method |
| title_fullStr | Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method |
| title_full_unstemmed | Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method |
| title_short | Online state of charge and state of health estimation for a lithium-ion battery based on a data–model fusion method |
| title_sort | online state of charge and state of health estimation for a lithium ion battery based on a data model fusion method |
| topic | State of Health State of Charge DRNTU::Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/88313 http://hdl.handle.net/10220/45668 |
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