Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization
Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent esti...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/11/6/207 |
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| author | Chen Fei Zhuo Lu Weiwei Jiang Liang Zhao Fan Zhang |
| author_facet | Chen Fei Zhuo Lu Weiwei Jiang Liang Zhao Fan Zhang |
| author_sort | Chen Fei |
| collection | DOAJ |
| description | Due to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent estimation approach that integrates data visualization and advanced machine learning techniques. Initially, the battery data are visualized using matplotlib to extract key features such as temperature difference, voltage difference, and average voltage. Subsequently, an XGBoost-based model is constructed to perform the initial SOH estimation. To further enhance the estimation accuracy, we introduce the Autoregressive Integrated Moving Average Model (ARIMA) model for post-estimation correction, effectively refining the preliminary results. Experimental results demonstrate that the proposed XGBoost–ARIMA model outperforms traditional algorithms, including Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), not only in estimation accuracy but also in generalization capability, showing significant improvements over five other regression models. |
| format | Article |
| id | doaj-art-d2f2aa0064334a4ca9deac722e2ecc9c |
| institution | Kabale University |
| issn | 2313-0105 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-d2f2aa0064334a4ca9deac722e2ecc9c2025-08-20T03:26:57ZengMDPI AGBatteries2313-01052025-05-0111620710.3390/batteries11060207Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint OptimizationChen Fei0Zhuo Lu1Weiwei Jiang2Liang Zhao3Fan Zhang4Nocommssioned Officer Academy of Pap, Hangzhou 311400, ChinaKey Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaSchool of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNocommssioned Officer Academy of Pap, Hangzhou 311400, ChinaNocommssioned Officer Academy of Pap, Hangzhou 311400, ChinaDue to the complex electrochemical reactions within lithium-ion batteries and the uncertainties with respect to external environmental factors, accurately assessing their State of Health (SOH) remains a significant challenge. To improve the precision of SOH estimation, we propose an intelligent estimation approach that integrates data visualization and advanced machine learning techniques. Initially, the battery data are visualized using matplotlib to extract key features such as temperature difference, voltage difference, and average voltage. Subsequently, an XGBoost-based model is constructed to perform the initial SOH estimation. To further enhance the estimation accuracy, we introduce the Autoregressive Integrated Moving Average Model (ARIMA) model for post-estimation correction, effectively refining the preliminary results. Experimental results demonstrate that the proposed XGBoost–ARIMA model outperforms traditional algorithms, including Linear Regression (LR), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), not only in estimation accuracy but also in generalization capability, showing significant improvements over five other regression models.https://www.mdpi.com/2313-0105/11/6/207lithium-ion batteryfeature extractionstate of health estimationXGBoost algorithmARIMA model |
| spellingShingle | Chen Fei Zhuo Lu Weiwei Jiang Liang Zhao Fan Zhang Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization Batteries lithium-ion battery feature extraction state of health estimation XGBoost algorithm ARIMA model |
| title | Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization |
| title_full | Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization |
| title_fullStr | Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization |
| title_full_unstemmed | Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization |
| title_short | Research on Lithium-Ion Battery State of Health Prediction Based on XGBoost–ARIMA Joint Optimization |
| title_sort | research on lithium ion battery state of health prediction based on xgboost arima joint optimization |
| topic | lithium-ion battery feature extraction state of health estimation XGBoost algorithm ARIMA model |
| url | https://www.mdpi.com/2313-0105/11/6/207 |
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