Rapid estimation method of lithium battery state of health based on novel health feature
The online estimation of the state of health (SOH) is an essential part of a lithium battery management system. Most data-driven lithium battery SOH estimation methods are computationally intensive and difficult to use in real-time in battery management system microcontrollers. Therefore, a rapid es...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Power Engineering Technology
2025-01-01
|
| Series: | 电力工程技术 |
| Subjects: | |
| Online Access: | https://www.epet-info.com/dlgcjsen/article/abstract/231229595 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823865258075226112 |
|---|---|
| author | DONG Xiaohong DONG Jinbo WANG Mingshen ZENG Fei PAN Yi |
| author_facet | DONG Xiaohong DONG Jinbo WANG Mingshen ZENG Fei PAN Yi |
| author_sort | DONG Xiaohong |
| collection | DOAJ |
| description | The online estimation of the state of health (SOH) is an essential part of a lithium battery management system. Most data-driven lithium battery SOH estimation methods are computationally intensive and difficult to use in real-time in battery management system microcontrollers. Therefore, a rapid estimation method of lithium battery SOH based on novel health feature is proposed in this paper. The charging data of the battery is firstly analyzed in the method, and based on the existing health characteristics of time interval of an equal charging voltage difference (TIECVD) in the constant current charging process of the battery, constructs a new health feature, that is, the health feature of charging voltage at the same starting point and charging time interval. Then, a fast estimation method of lithium battery SOH based on the novel health feature and multiple linear regression (MLR) is proposed. Next, by analyzing the oxford battery aging dataset and the random usage dataset of lithium ion batteries used by NASA, the method traverses the constant current charging voltage range in steps of 0.01 V and determines the optimal starting voltage of the lithium battery by maximizing the Pearson correlation coefficient. Finally, considering different time intervals, the method uses the ordinary least squares (OLS) regression analysis method to determine the optimal time interval parameter of the lithium battery. The training set divided by two datasets is used to establish a multiple linear regression model, and the validation set divided by two datasets is used to verify the method. The experimental results show that the proposed method and novel health feature can greatly reduce the calculation volume, and can achieve fast estimation of lithium battery SOH while ensuring prediction accuracy. |
| format | Article |
| id | doaj-art-7fd6d23ed301439bbc5c9b78b8538fee |
| institution | Kabale University |
| issn | 2096-3203 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Department of Electric Power Engineering Technology |
| record_format | Article |
| series | 电力工程技术 |
| spelling | doaj-art-7fd6d23ed301439bbc5c9b78b8538fee2025-02-08T08:40:18ZzhoEditorial Department of Electric Power Engineering Technology电力工程技术2096-32032025-01-0144113614210.12158/j.2096-3203.2025.01.014231229595Rapid estimation method of lithium battery state of health based on novel health featureDONG Xiaohong0DONG Jinbo1WANG Mingshen2ZENG Fei3PAN Yi4School of Electrical Engineering, Hebei University of Technology, Tianjin 300130, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, ChinaState Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, ChinaState Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, ChinaState Grid Jiangsu Electric Power Co., Ltd. Research Institute, Nanjing 211103, ChinaThe online estimation of the state of health (SOH) is an essential part of a lithium battery management system. Most data-driven lithium battery SOH estimation methods are computationally intensive and difficult to use in real-time in battery management system microcontrollers. Therefore, a rapid estimation method of lithium battery SOH based on novel health feature is proposed in this paper. The charging data of the battery is firstly analyzed in the method, and based on the existing health characteristics of time interval of an equal charging voltage difference (TIECVD) in the constant current charging process of the battery, constructs a new health feature, that is, the health feature of charging voltage at the same starting point and charging time interval. Then, a fast estimation method of lithium battery SOH based on the novel health feature and multiple linear regression (MLR) is proposed. Next, by analyzing the oxford battery aging dataset and the random usage dataset of lithium ion batteries used by NASA, the method traverses the constant current charging voltage range in steps of 0.01 V and determines the optimal starting voltage of the lithium battery by maximizing the Pearson correlation coefficient. Finally, considering different time intervals, the method uses the ordinary least squares (OLS) regression analysis method to determine the optimal time interval parameter of the lithium battery. The training set divided by two datasets is used to establish a multiple linear regression model, and the validation set divided by two datasets is used to verify the method. The experimental results show that the proposed method and novel health feature can greatly reduce the calculation volume, and can achieve fast estimation of lithium battery SOH while ensuring prediction accuracy.https://www.epet-info.com/dlgcjsen/article/abstract/231229595lithium batterystate of health (soh) estimationnovel health featuredata-driven approachmultiple linear regression (mlr)charging voltage data fragment |
| spellingShingle | DONG Xiaohong DONG Jinbo WANG Mingshen ZENG Fei PAN Yi Rapid estimation method of lithium battery state of health based on novel health feature 电力工程技术 lithium battery state of health (soh) estimation novel health feature data-driven approach multiple linear regression (mlr) charging voltage data fragment |
| title | Rapid estimation method of lithium battery state of health based on novel health feature |
| title_full | Rapid estimation method of lithium battery state of health based on novel health feature |
| title_fullStr | Rapid estimation method of lithium battery state of health based on novel health feature |
| title_full_unstemmed | Rapid estimation method of lithium battery state of health based on novel health feature |
| title_short | Rapid estimation method of lithium battery state of health based on novel health feature |
| title_sort | rapid estimation method of lithium battery state of health based on novel health feature |
| topic | lithium battery state of health (soh) estimation novel health feature data-driven approach multiple linear regression (mlr) charging voltage data fragment |
| url | https://www.epet-info.com/dlgcjsen/article/abstract/231229595 |
| work_keys_str_mv | AT dongxiaohong rapidestimationmethodoflithiumbatterystateofhealthbasedonnovelhealthfeature AT dongjinbo rapidestimationmethodoflithiumbatterystateofhealthbasedonnovelhealthfeature AT wangmingshen rapidestimationmethodoflithiumbatterystateofhealthbasedonnovelhealthfeature AT zengfei rapidestimationmethodoflithiumbatterystateofhealthbasedonnovelhealthfeature AT panyi rapidestimationmethodoflithiumbatterystateofhealthbasedonnovelhealthfeature |