A Review of SOH Prediction of Li-Ion Batteries Based on Data-Driven Algorithms
As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contras...
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| Vydáno v: | Energies (Basel) Ročník 16; číslo 7; s. 3167 |
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| Jazyk: | angličtina |
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Basel
MDPI AG
01.04.2023
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| ISSN: | 1996-1073, 1996-1073 |
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| Abstract | As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area. |
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| AbstractList | As an important energy storage device, lithium-ion batteries (LIBs) have been widely used in various fields due to their remarkable advantages. The high level of precision in estimating the battery’s state of health greatly enhances the safety and dependability of the application process. In contrast to traditional model-based prediction methods that are complex and have limited accuracy, data-driven prediction methods, which are considered mainstream, rely on direct data analysis and offer higher accuracy. Therefore, this paper reviews how to use the latest data-driven algorithms to predict the SOH of LIBs, and proposes a general prediction process, including the acquisition of datasets for the charging and discharging process of LIBs, the processing of data and features, and the selection of algorithms. The advantages and limitations of various processing methods and cutting-edge data-driven algorithms are summarized and compared, and methods with potential applications are proposed. Effort was also made to point out their application methods and application scenarios, providing guidance for researchers in this area. |
| Audience | Academic |
| Author | Du, Jiaxuan Yang, Dongfang Li, Liwei Sun, Hanlei Wang, Kai Wang, Licheng Zhang, Ming |
| Author_xml | – sequence: 1 givenname: Ming surname: Zhang fullname: Zhang, Ming – sequence: 2 givenname: Dongfang surname: Yang fullname: Yang, Dongfang – sequence: 3 givenname: Jiaxuan surname: Du fullname: Du, Jiaxuan – sequence: 4 givenname: Hanlei surname: Sun fullname: Sun, Hanlei – sequence: 5 givenname: Liwei surname: Li fullname: Li, Liwei – sequence: 6 givenname: Licheng surname: Wang fullname: Wang, Licheng – sequence: 7 givenname: Kai orcidid: 0000-0002-3513-3511 surname: Wang fullname: Wang, Kai |
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| SubjectTerms | Accuracy Aging Algorithms Analysis Batteries Data processing data-driven algorithms Datasets Decomposition Electrodes Electrolytes Energy industry Graphite Information management LIB Lithium Machine learning Methods Neural networks Power SOH |
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