Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost
To mine the battery’s health factors more comprehensively and accurately identify the lithium battery’s State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/15/16/5981 |
| _version_ | 1797445735694729216 |
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| author | Mei Zhang Wanli Chen Jun Yin Tao Feng |
| author_facet | Mei Zhang Wanli Chen Jun Yin Tao Feng |
| author_sort | Mei Zhang |
| collection | DOAJ |
| description | To mine the battery’s health factors more comprehensively and accurately identify the lithium battery’s State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve the problem that the traditional Douglas–Peucker algorithm has difficulties extracting curve features in a fixed dimension, the Douglas–Peucker algorithm is improved by de-thresholding. Then, the Wrapper method combined with the Improved Douglas–Peucker algorithm is used to construct the feature engineering of battery life prediction, and the optimal feature subset is obtained. Then, LAOS-XGboost is used to establish a battery SOH prediction model; finally, this model is used to predict the SOH of different batteries and the same battery, and the robustness of the model is analyzed. The experimental results show that the R2 of all XGboost models is higher than 0.97 in the prediction experiments of different batteries. The AE of the LAOS-XGboost model is 0, and the TIC index is less than 3% under 10 dB SNR. In the same battery prediction experiment, the TIC index of the model is less than 0.3%. |
| first_indexed | 2024-03-09T13:30:07Z |
| format | Article |
| id | doaj.art-5a1d2ab8a80f4d499738f8cd3513ea2b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T13:30:07Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-5a1d2ab8a80f4d499738f8cd3513ea2b2023-11-30T21:18:56ZengMDPI AGEnergies1996-10732022-08-011516598110.3390/en15165981Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboostMei Zhang0Wanli Chen1Jun Yin2Tao Feng3College of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, ChinaCollege of Electrical and Information Engineering, Anhui University of Science and Technology (AUST), Huainan 232001, ChinaTo mine the battery’s health factors more comprehensively and accurately identify the lithium battery’s State of Health (SOH), an Improved Douglas–Peucker feature extraction algorithm is proposed, and the LAOS-XGboost model is proposed to be used to predict the SOH of the battery. Firstly, to solve the problem that the traditional Douglas–Peucker algorithm has difficulties extracting curve features in a fixed dimension, the Douglas–Peucker algorithm is improved by de-thresholding. Then, the Wrapper method combined with the Improved Douglas–Peucker algorithm is used to construct the feature engineering of battery life prediction, and the optimal feature subset is obtained. Then, LAOS-XGboost is used to establish a battery SOH prediction model; finally, this model is used to predict the SOH of different batteries and the same battery, and the robustness of the model is analyzed. The experimental results show that the R2 of all XGboost models is higher than 0.97 in the prediction experiments of different batteries. The AE of the LAOS-XGboost model is 0, and the TIC index is less than 3% under 10 dB SNR. In the same battery prediction experiment, the TIC index of the model is less than 0.3%.https://www.mdpi.com/1996-1073/15/16/5981lithium-ion batterySOH predictionXGboostDouglas–Peucker algorithmLAOS |
| spellingShingle | Mei Zhang Wanli Chen Jun Yin Tao Feng Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost Energies lithium-ion battery SOH prediction XGboost Douglas–Peucker algorithm LAOS |
| title | Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost |
| title_full | Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost |
| title_fullStr | Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost |
| title_full_unstemmed | Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost |
| title_short | Lithium Battery Health Factor Extraction Based on Improved Douglas–Peucker Algorithm and SOH Prediction Based on XGboost |
| title_sort | lithium battery health factor extraction based on improved douglas peucker algorithm and soh prediction based on xgboost |
| topic | lithium-ion battery SOH prediction XGboost Douglas–Peucker algorithm LAOS |
| url | https://www.mdpi.com/1996-1073/15/16/5981 |
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