A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model
The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable...
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| Format: | Article |
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MDPI AG
2019-04-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/12/7/1349 |
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| author | Qiaohua Fang Xuezhe Wei Tianyi Lu Haifeng Dai Jiangong Zhu |
| author_facet | Qiaohua Fang Xuezhe Wei Tianyi Lu Haifeng Dai Jiangong Zhu |
| author_sort | Qiaohua Fang |
| collection | DOAJ |
| description | The state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li<sub>1</sub>(NiCoAl)<sub>1</sub>O<sub>2</sub>-Li<sub>1</sub>(NiCoMn)<sub>1</sub>O<sub>2</sub>/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the <i>α</i>-based, <i>β</i>-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method. |
| first_indexed | 2024-12-10T07:35:01Z |
| format | Article |
| id | doaj.art-4948e85e69a74a30b53d5fd4e403867a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-12-10T07:35:01Z |
| publishDate | 2019-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-4948e85e69a74a30b53d5fd4e403867a2022-12-22T01:57:26ZengMDPI AGEnergies1996-10732019-04-01127134910.3390/en12071349en12071349A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation ModelQiaohua Fang0Xuezhe Wei1Tianyi Lu2Haifeng Dai3Jiangong Zhu4Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaClean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, ChinaThe state of health estimation for lithium-ion battery is a key function of the battery management system. Unlike the traditional state of health estimation methods under dynamic conditions, the relaxation process is studied and utilized to estimate the state of health in this research. A reasonable and accurate voltage relaxation model is established based on the linear relationship between time coefficient and open circuit time for a Li<sub>1</sub>(NiCoAl)<sub>1</sub>O<sub>2</sub>-Li<sub>1</sub>(NiCoMn)<sub>1</sub>O<sub>2</sub>/graphite battery. The accuracy and effectiveness of the model is verified under different states of charge and states of health. Through systematic experiments under different states of charge and states of health, it is found that the model parameters monotonically increase with the aging of the battery. Three different capacity estimation methods are proposed based on the relationship between model parameters and residual capacity, namely the <i>α</i>-based, <i>β</i>-based, and parameter–fusion methods. The validation of the three methods is verified with high accuracy. The results indicate that the capacity estimation error under most of the aging states is less than 1%. The largest error drops from 3% under the α-based method to 1.8% under the parameter–fusion method.https://www.mdpi.com/1996-1073/12/7/1349voltage relaxation modelcapacity estimationlithium-ion batterybattery management system |
| spellingShingle | Qiaohua Fang Xuezhe Wei Tianyi Lu Haifeng Dai Jiangong Zhu A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model Energies voltage relaxation model capacity estimation lithium-ion battery battery management system |
| title | A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model |
| title_full | A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model |
| title_fullStr | A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model |
| title_full_unstemmed | A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model |
| title_short | A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model |
| title_sort | state of health estimation method for lithium ion batteries based on voltage relaxation model |
| topic | voltage relaxation model capacity estimation lithium-ion battery battery management system |
| url | https://www.mdpi.com/1996-1073/12/7/1349 |
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