State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration
An accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging curren...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/13/8/148 |
| _version_ | 1797407381473198080 |
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| author | Laijin Luo Chaolong Zhang Youhui Tian Huihan Liu |
| author_facet | Laijin Luo Chaolong Zhang Youhui Tian Huihan Liu |
| author_sort | Laijin Luo |
| collection | DOAJ |
| description | An accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation. |
| first_indexed | 2024-03-09T03:40:35Z |
| format | Article |
| id | doaj.art-94269e8aac43404db9ea763160fe6e61 |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-09T03:40:35Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-94269e8aac43404db9ea763160fe6e612023-12-03T14:41:20ZengMDPI AGWorld Electric Vehicle Journal2032-66532022-08-0113814810.3390/wevj13080148State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging DurationLaijin Luo0Chaolong Zhang1Youhui Tian2Huihan Liu3School of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, ChinaSchool of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, ChinaSchool of Intelligent Engineering Technology, Jiangsu Vocational Institute of Commerce, Nanjing 211168, ChinaSchool of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, ChinaAn accurate state-of-health (SOH) estimation is vital to guarantee the safety and reliability of a lithium-ion battery management system. In application, the electrical vehicles generally start charging when the battery is at a non-zero state of charge (SOC), which will influence the charging current, voltage and duration, greatly hindering many traditional health features to estimate the SOH. However, the constant voltage charging phase is not limited by the previous non-zero SOC starting charge. In order to overcome the difficulty, a method of estimating the battery SOH based on the information entropy of battery currents of the constant voltage charging phase and charging duration is proposed. Firstly, the time series of charging current data from the constant voltage phase are measured, and then the information entropy of battery currents and charging time are calculated as new indicators. The penalty coefficient and width factor of a support vector machine (SVM) improved by the sparrow search algorithm is utilized to establish the underlying mapping relationships between the current entropy, charging duration and battery SOH. Additionally, the results indicate the adaptability and effectiveness of the proposed approach for a battery pack and cell SOH estimation.https://www.mdpi.com/2032-6653/13/8/148lithium-ion batterystate of healthstate of chargecurrent entropysparrow search algorithmsupport vector machine |
| spellingShingle | Laijin Luo Chaolong Zhang Youhui Tian Huihan Liu State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration World Electric Vehicle Journal lithium-ion battery state of health state of charge current entropy sparrow search algorithm support vector machine |
| title | State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration |
| title_full | State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration |
| title_fullStr | State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration |
| title_full_unstemmed | State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration |
| title_short | State-of-Health Estimate for the Lithium-Ion Battery Based on Constant Voltage Current Entropy and Charging Duration |
| title_sort | state of health estimate for the lithium ion battery based on constant voltage current entropy and charging duration |
| topic | lithium-ion battery state of health state of charge current entropy sparrow search algorithm support vector machine |
| url | https://www.mdpi.com/2032-6653/13/8/148 |
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