State of health estimation of LIB based on discharge section with multi-model combined
Accurate estimation of a battery's state of health (SOH) is essential in battery management systems (BMS). This study considers a complete analysis of combining incremental capacity (IC), differential thermal voltammetry (DTV), and differential temperature (DT) for SOH prediction in cases of di...
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| Language: | English |
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Elsevier
2024-02-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024018395 |
| _version_ | 1797267756894126080 |
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| author | Peng Xu Yuan Huang Wenwen Ran Shibin Wan Cheng Guo Xin Su Libing Yuan Yuanhong Dan |
| author_facet | Peng Xu Yuan Huang Wenwen Ran Shibin Wan Cheng Guo Xin Su Libing Yuan Yuanhong Dan |
| author_sort | Peng Xu |
| collection | DOAJ |
| description | Accurate estimation of a battery's state of health (SOH) is essential in battery management systems (BMS). This study considers a complete analysis of combining incremental capacity (IC), differential thermal voltammetry (DTV), and differential temperature (DT) for SOH prediction in cases of discharge. Initially, the IC, DTV, and DT curves were derived from the current, voltage, and temperature datasets, and these curves underwent smoothing through the application of Lowess and Gaussian techniques. Subsequently, discerning healthy features were identified within the domains where the curve exhibited substantial phase transitions. Utilizing Pearson correlation analysis, features exhibiting the utmost correlation with battery capacity degradation were singled out. Finally, the state-of-health (SOH) prediction model was constructed using a bidirectional long short-term memory (BILSTM) neural network. Two datasets were used to validate the model, and the experimental results demonstrated that the SOH prediction had a root mean square error (RMSE) below 1.2% and mean absolute error (MAE) below 1%, which verified the feasibility and accuracy. This approach quantifies the internal electrochemical reactions of a battery using externally measured data, further enabling early SOH predictions. |
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| format | Article |
| id | doaj.art-a7fe4f17d25649089356c87e7a00edf8 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-25T01:21:39Z |
| publishDate | 2024-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-a7fe4f17d25649089356c87e7a00edf82024-03-09T09:26:16ZengElsevierHeliyon2405-84402024-02-01104e25808State of health estimation of LIB based on discharge section with multi-model combinedPeng Xu0Yuan Huang1Wenwen Ran2Shibin Wan3Cheng Guo4Xin Su5Libing Yuan6Yuanhong Dan7School of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, ChinaSchool of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, China; Corresponding author.School of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, ChinaSchool of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, ChinaSchool of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, ChinaSchool of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, ChinaSchool of Electrical and Electronics Engineering, Chongqing University of Technology, Banan, Chongqing, 400054, ChinaSchool of Computer Science and Technology, Chongqing University of Technology, Banan, Chongqing, 400054, China; Nanjing University of Science and Technology, Xuanwu, Nanjing, ChinaAccurate estimation of a battery's state of health (SOH) is essential in battery management systems (BMS). This study considers a complete analysis of combining incremental capacity (IC), differential thermal voltammetry (DTV), and differential temperature (DT) for SOH prediction in cases of discharge. Initially, the IC, DTV, and DT curves were derived from the current, voltage, and temperature datasets, and these curves underwent smoothing through the application of Lowess and Gaussian techniques. Subsequently, discerning healthy features were identified within the domains where the curve exhibited substantial phase transitions. Utilizing Pearson correlation analysis, features exhibiting the utmost correlation with battery capacity degradation were singled out. Finally, the state-of-health (SOH) prediction model was constructed using a bidirectional long short-term memory (BILSTM) neural network. Two datasets were used to validate the model, and the experimental results demonstrated that the SOH prediction had a root mean square error (RMSE) below 1.2% and mean absolute error (MAE) below 1%, which verified the feasibility and accuracy. This approach quantifies the internal electrochemical reactions of a battery using externally measured data, further enabling early SOH predictions.http://www.sciencedirect.com/science/article/pii/S2405844024018395Lithium-ion batteryState of healthBidirectional long short-term memory networkIncremental capacity analysisDifferential thermal voltammetry analysisDifferential temperature analysis |
| spellingShingle | Peng Xu Yuan Huang Wenwen Ran Shibin Wan Cheng Guo Xin Su Libing Yuan Yuanhong Dan State of health estimation of LIB based on discharge section with multi-model combined Heliyon Lithium-ion battery State of health Bidirectional long short-term memory network Incremental capacity analysis Differential thermal voltammetry analysis Differential temperature analysis |
| title | State of health estimation of LIB based on discharge section with multi-model combined |
| title_full | State of health estimation of LIB based on discharge section with multi-model combined |
| title_fullStr | State of health estimation of LIB based on discharge section with multi-model combined |
| title_full_unstemmed | State of health estimation of LIB based on discharge section with multi-model combined |
| title_short | State of health estimation of LIB based on discharge section with multi-model combined |
| title_sort | state of health estimation of lib based on discharge section with multi model combined |
| topic | Lithium-ion battery State of health Bidirectional long short-term memory network Incremental capacity analysis Differential thermal voltammetry analysis Differential temperature analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024018395 |
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