State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM
The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery volta...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/12/4/228 |
| _version_ | 1797499749407916032 |
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| author | Jianfeng Jiang Shaishai Zhao Chaolong Zhang |
| author_facet | Jianfeng Jiang Shaishai Zhao Chaolong Zhang |
| author_sort | Jianfeng Jiang |
| collection | DOAJ |
| description | The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons. |
| first_indexed | 2024-03-10T03:51:54Z |
| format | Article |
| id | doaj.art-1941aa7d56234354af38db833987d0ae |
| institution | Directory Open Access Journal |
| issn | 2032-6653 |
| language | English |
| last_indexed | 2024-03-10T03:51:54Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj.art-1941aa7d56234354af38db833987d0ae2023-11-23T11:03:27ZengMDPI AGWorld Electric Vehicle Journal2032-66532021-11-0112422810.3390/wevj12040228State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTMJianfeng Jiang0Shaishai Zhao1Chaolong Zhang2School of Intelligent Engineering Technology, Jiangsu Vocational Institute of Commerce, Nanjing 211168, ChinaSchool of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, ChinaSchool of Electronic Engineering and Intelligent Manufacturing, Anqing Normal University, Anqing 246011, ChinaThe state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.https://www.mdpi.com/2032-6653/12/4/228lithium-ion batteryhealth monitoringchi-squared statisticextreme learning machinelong short-term memory neural network |
| spellingShingle | Jianfeng Jiang Shaishai Zhao Chaolong Zhang State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM World Electric Vehicle Journal lithium-ion battery health monitoring chi-squared statistic extreme learning machine long short-term memory neural network |
| title | State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM |
| title_full | State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM |
| title_fullStr | State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM |
| title_full_unstemmed | State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM |
| title_short | State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM |
| title_sort | state of health estimate for the lithium ion battery using chi square and elm lstm |
| topic | lithium-ion battery health monitoring chi-squared statistic extreme learning machine long short-term memory neural network |
| url | https://www.mdpi.com/2032-6653/12/4/228 |
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