Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm
Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence ext...
| Main Authors: | , , , , , , |
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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/1996-1073/15/16/5842 |
| _version_ | 1797432259287973888 |
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| author | Shuo Sun Qianli Zhang Junzhong Sun Wei Cai Zhiyong Zhou Zhanlu Yang Zongliang Wang |
| author_facet | Shuo Sun Qianli Zhang Junzhong Sun Wei Cai Zhiyong Zhou Zhanlu Yang Zongliang Wang |
| author_sort | Shuo Sun |
| collection | DOAJ |
| description | Research on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model. |
| first_indexed | 2024-03-09T09:57:48Z |
| format | Article |
| id | doaj.art-d663d27750774f82a792839ad450ebb1 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T09:57:48Z |
| publishDate | 2022-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d663d27750774f82a792839ad450ebb12023-12-01T23:39:20ZengMDPI AGEnergies1996-10732022-08-011516584210.3390/en15165842Lead–Acid Battery SOC Prediction Using Improved AdaBoost AlgorithmShuo Sun0Qianli Zhang1Junzhong Sun2Wei Cai3Zhiyong Zhou4Zhanlu Yang5Zongliang Wang6Department of Power Manipulation, Navy Submarine Academy, Qingdao 266042, ChinaCollege of Engineering, Ocean University of China, Qingdao 266042, ChinaDepartment of Power Manipulation, Navy Submarine Academy, Qingdao 266042, ChinaDepartment of Power Manipulation, Navy Submarine Academy, Qingdao 266042, ChinaDepartment of Power Manipulation, Navy Submarine Academy, Qingdao 266042, ChinaDepartment of Power Manipulation, Navy Submarine Academy, Qingdao 266042, ChinaDepartment of Power Manipulation, Navy Submarine Academy, Qingdao 266042, ChinaResearch on the state of charge (SOC) prediction of lead–acid batteries is of great importance to the use and management of batteries. Due to this reason, this paper proposes a method for predicting the SOC of lead–acid batteries based on the improved AdaBoost model. By using the online sequence extreme learning machine (OSELM) as its weak learning machine, this model can achieve incremental learning of the model, which has a high computational efficiency, and does not require repeated training of old samples. Through improvement of the AdaBoost algorithm, the local prediction accuracy of the algorithm for the sample is enhanced, the scores of the proposed model in the maximum absolute error (AEmax) and maximum absolute percent error (APEmax) indicators are 6.8% and 8.8% lower, and the accuracy of the model is further improved. According to the verification with experimental data, when there are a large number of prediction samples, the improved AdaBoost model can reduce the prediction accuracy indicators of mean absolute percent error (MAPE), mean absolute error (MAE), and mean square error (MSE) to 75.4%, 58.3, and 84.2%, respectively. Compared with various other prediction methods in the prediction accuracy of battery SOC, the prediction accuracy indicators MAE, MSE, MAPE, AEmax, and APEmax of the model proposed in this paper are all optimal, which proves the validity and adaptive ability of the model.https://www.mdpi.com/1996-1073/15/16/5842lead–acid batterystate of charge (SOC)AdaBoost algorithmonline sequence extreme learning machine (OSELM)incremental learning |
| spellingShingle | Shuo Sun Qianli Zhang Junzhong Sun Wei Cai Zhiyong Zhou Zhanlu Yang Zongliang Wang Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm Energies lead–acid battery state of charge (SOC) AdaBoost algorithm online sequence extreme learning machine (OSELM) incremental learning |
| title | Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm |
| title_full | Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm |
| title_fullStr | Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm |
| title_full_unstemmed | Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm |
| title_short | Lead–Acid Battery SOC Prediction Using Improved AdaBoost Algorithm |
| title_sort | lead acid battery soc prediction using improved adaboost algorithm |
| topic | lead–acid battery state of charge (SOC) AdaBoost algorithm online sequence extreme learning machine (OSELM) incremental learning |
| url | https://www.mdpi.com/1996-1073/15/16/5842 |
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