State Identification of Charging Module Based on SN‐EMD‐SSEE and DBO‐HKELM
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Wiley Open Access
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| Natura: | Artículo Open Access |
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Wiley
2025
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| _version_ | 1867017317488001024 |
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| author | Bingyu Li Xianhai Pang Xuhao Du Ziwen Cai |
| author_facet | Bingyu Li Xianhai Pang Xuhao Du Ziwen Cai Bingyu Li Xianhai Pang Xuhao Du Ziwen Cai |
| collection | Wiley Open Access |
| contents | State Identification of Charging Module Based on SN‐EMD‐SSEE and DBO‐HKELM Bingyu Li Xianhai Pang Xuhao Du Ziwen Cai Engineering Reports ABSTRACTLithium‐ion batteries are widely used in emergency back‐up power supply with its superior performance, and they are usually used with a charging module. The charging module is usually composed of power electronic devices, but failures may occur in power electronic devicesdue to device aging and mechanical vibration in the complex environment; it will result in huge economic losses. However, the protection function of the charging module covers short fault, over‐voltage fault, and over‐current at system level rather than the open‐circuit fault of MOSFETs and diodes at component level, which leads to hidden fire danger or accident risks. To address this issue, this paper improved a new state identification method of charging module based on signal normalization (SN) which is specially designed, empirical mode decomposition (EMD), sequence signal entropy extraction (SSEE), hybrid kernel extreme learning machine (HKELM), and dung beetle optimization (DBO). In basic work, 10 measurable variables and 23 fault states are determined by simulating open‐circuit faults at MOSFETs and diodes. In data pre‐processing, SN‐EMD‐SSEE is developed to extract state characteristics values for high adaptability to full working condition and high significance for easy identification. In modeling, DBO‐HKELM identification model is constructed by improving ELM (Extreme Learning Machine) and optimizing parameters based on DBO 60 state characteristics values and 24 states including normal state, which are, respectively, used as the input and output of the identification model. In verification, the proposed state identification method based on SN‐EMD‐SSEE and DBO‐HKELM is embedded into the control chip of charging module to obtain fault state code in real time. The experimental results show that the proposed state identification method is robust, and its identification accuracy is up to 94.7%. 10.1002/eng2.70039 http://creativecommons.org/licenses/by/4.0/ |
| doi_str_mv | 10.1002/eng2.70039 |
| format | Artículo Open Access |
| id | wiley_oa_10_1002_eng2_70039 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by/4.0/ |
| publishDate | 2025 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | State Identification of Charging Module Based on SN‐EMD‐SSEE and DBO‐HKELM Bingyu Li Xianhai Pang Xuhao Du Ziwen Cai Engineering Reports State Identification of Charging Module Based on SN‐EMD‐SSEE and DBO‐HKELM Bingyu Li Xianhai Pang Xuhao Du Ziwen Cai Engineering Reports ABSTRACTLithium‐ion batteries are widely used in emergency back‐up power supply with its superior performance, and they are usually used with a charging module. The charging module is usually composed of power electronic devices, but failures may occur in power electronic devicesdue to device aging and mechanical vibration in the complex environment; it will result in huge economic losses. However, the protection function of the charging module covers short fault, over‐voltage fault, and over‐current at system level rather than the open‐circuit fault of MOSFETs and diodes at component level, which leads to hidden fire danger or accident risks. To address this issue, this paper improved a new state identification method of charging module based on signal normalization (SN) which is specially designed, empirical mode decomposition (EMD), sequence signal entropy extraction (SSEE), hybrid kernel extreme learning machine (HKELM), and dung beetle optimization (DBO). In basic work, 10 measurable variables and 23 fault states are determined by simulating open‐circuit faults at MOSFETs and diodes. In data pre‐processing, SN‐EMD‐SSEE is developed to extract state characteristics values for high adaptability to full working condition and high significance for easy identification. In modeling, DBO‐HKELM identification model is constructed by improving ELM (Extreme Learning Machine) and optimizing parameters based on DBO 60 state characteristics values and 24 states including normal state, which are, respectively, used as the input and output of the identification model. In verification, the proposed state identification method based on SN‐EMD‐SSEE and DBO‐HKELM is embedded into the control chip of charging module to obtain fault state code in real time. The experimental results show that the proposed state identification method is robust, and its identification accuracy is up to 94.7%. 10.1002/eng2.70039 http://creativecommons.org/licenses/by/4.0/ |
| title | State Identification of Charging Module Based on SN‐EMD‐SSEE and DBO‐HKELM |
| topic | Engineering Reports |
| url | https://onlinelibrary.wiley.com/doi/10.1002/eng2.70039 |