Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule
Lithium-ion batteries are extensively utilized in diverse sectors such as automotive applications. The imprecise estimation of the State Of Health (SOH) will significantly impact safe operations and cost reduction initiatives. Addressing challenges in extracting aging features and the complexity of...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10701285/ |
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| author | Liu Zhang Bo Xing Yanbing Gao Lei Yao Dengfeng Zhao Jinquan Ding Yanyan Li |
| author_facet | Liu Zhang Bo Xing Yanbing Gao Lei Yao Dengfeng Zhao Jinquan Ding Yanyan Li |
| author_sort | Liu Zhang |
| collection | DOAJ |
| description | Lithium-ion batteries are extensively utilized in diverse sectors such as automotive applications. The imprecise estimation of the State Of Health (SOH) will significantly impact safe operations and cost reduction initiatives. Addressing challenges in extracting aging features and the complexity of modeling, this study proposed a feature parameter identification method leveraging Incremental Capacity Analysis (ICA) technology and the elbow rule. This approach employed data-driven methodologies to predict battery health status. Initially, voltage capacity and incremental capacity curves were obtained, and data-driven principles were employed along with data cleaning to mitigate noise. Subsequently, correlation and significance analysis methods were applied for preliminary health feature selection. To eliminate data redundancy, a novel principal component analysis strategy based on the elbow optimization rule was introduced. Next, two data-driven estimation models, namely BP (Back Propagation) neural network and Gaussian Process Regression (GPR), were established. These models were employed for SOH prediction. Comparative results demonstrate that feature parameters extracted using the elbow rule from both models closely approximate reality, validating the correctness and accuracy of the feature extraction method. Specifically, the GPR model exhibited higher prediction accuracy. Furthermore, the GPR model was applied to predict SOH for an additional set of three batteries. The findings reveal a relative error of approximately 4% when maintaining lithium-ion battery SOH at 80%, affirming the GPR model’s high accuracy and robust adaptability for SOH prediction. |
| format | Article |
| id | doaj-art-4cfa6392ee2f4bf9a1840800a6144bc1 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
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| spelling | doaj-art-4cfa6392ee2f4bf9a1840800a6144bc12025-01-16T00:01:58ZengIEEEIEEE Access2169-35362024-01-011218358118359510.1109/ACCESS.2024.347177710701285Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow RuleLiu Zhang0https://orcid.org/0009-0007-8413-5592Bo Xing1Yanbing Gao2Lei Yao3Dengfeng Zhao4Jinquan Ding5Yanyan Li6Mechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaNational Quality Inspection and Testing Center for Abrasives, Zhengzhou, Henan, ChinaNational Quality Inspection and Testing Center for Abrasives, Zhengzhou, Henan, ChinaNew Energy College, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaMechanical and Electrical Engineering Institute, Zhengzhou University of Light Industry, Zhengzhou, Henan, ChinaLithium-ion batteries are extensively utilized in diverse sectors such as automotive applications. The imprecise estimation of the State Of Health (SOH) will significantly impact safe operations and cost reduction initiatives. Addressing challenges in extracting aging features and the complexity of modeling, this study proposed a feature parameter identification method leveraging Incremental Capacity Analysis (ICA) technology and the elbow rule. This approach employed data-driven methodologies to predict battery health status. Initially, voltage capacity and incremental capacity curves were obtained, and data-driven principles were employed along with data cleaning to mitigate noise. Subsequently, correlation and significance analysis methods were applied for preliminary health feature selection. To eliminate data redundancy, a novel principal component analysis strategy based on the elbow optimization rule was introduced. Next, two data-driven estimation models, namely BP (Back Propagation) neural network and Gaussian Process Regression (GPR), were established. These models were employed for SOH prediction. Comparative results demonstrate that feature parameters extracted using the elbow rule from both models closely approximate reality, validating the correctness and accuracy of the feature extraction method. Specifically, the GPR model exhibited higher prediction accuracy. Furthermore, the GPR model was applied to predict SOH for an additional set of three batteries. The findings reveal a relative error of approximately 4% when maintaining lithium-ion battery SOH at 80%, affirming the GPR model’s high accuracy and robust adaptability for SOH prediction.https://ieeexplore.ieee.org/document/10701285/BP neural networkincremental capacityelbow ruleGaussian process regressionlithium-ion battery |
| spellingShingle | Liu Zhang Bo Xing Yanbing Gao Lei Yao Dengfeng Zhao Jinquan Ding Yanyan Li Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule IEEE Access BP neural network incremental capacity elbow rule Gaussian process regression lithium-ion battery |
| title | Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule |
| title_full | Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule |
| title_fullStr | Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule |
| title_full_unstemmed | Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule |
| title_short | Data-Driven Prediction Methods for Lithium-Ion Battery State of Health Based on Elbow Rule |
| title_sort | data driven prediction methods for lithium ion battery state of health based on elbow rule |
| topic | BP neural network incremental capacity elbow rule Gaussian process regression lithium-ion battery |
| url | https://ieeexplore.ieee.org/document/10701285/ |
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