L773:2590 1168
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| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 04105naa a2200517 4500 | |
| 001 | oai:DiVA.org:mdh-68071 | |
| 003 | SwePub | |
| 008 | 240717s2024 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-680712 URI |
| 024 | 7 | a https://doi.org/10.1016/j.etran.2024.1003472 DOI |
| 040 | a (SwePub)mdh | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Sun, Y.u School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China4 aut |
| 245 | 1 0 | a Battery degradation evaluation based on impedance spectra using a limited number of voltage-capacity curves |
| 264 | 1 | b Elsevier,c 2024 |
| 338 | a print2 rdacarrier | |
| 520 | a Degradation prediction is crucial for ensuring safe and reliable operation of batteries. However, relying solely on capacity to characterize aging cannot comprehensively represent the health status of the battery. This work explores the potential of using a limited number of partial voltage-capacity curves to evaluate battery degradation with the aid of deep learning approaches, which can be used for onboard applications. A sequence-to-sequence model is proposed to predict the electrochemical impedance spectra during battery degradation. It only uses capacity sequences within a specific voltage range at fixed voltage increments from a limited number of cycles, which can be flexibly adapted to different life stages in an end-to-end manner. The proposed method has been validated based on the developed degradation dataset. The root mean square errors for the prediction of impedance spectra are less than 1.48 mΩ. Capacities and resistances associated with electrochemical processes can be further extracted from the obtained impedance spectra, facilitating a comprehensive evaluation of battery degradation. As a limited number of measured data are needed, the proposed method can reduce data storage requirements and computational demands, which enables fast and comprehensive aging diagnosis. | |
| 650 | 7 | a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Annan elektroteknik och elektronik0 (SwePub)202992 hsv//swe |
| 650 | 7 | a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Other Electrical Engineering, Electronic Engineering, Information Engineering0 (SwePub)202992 hsv//eng |
| 653 | a Aging diagnosis | |
| 653 | a Battery degradation | |
| 653 | a Deep learning | |
| 653 | a Impedance spectra | |
| 653 | a Digital storage | |
| 653 | a Electric batteries | |
| 653 | a Mean square error | |
| 653 | a Capacity curves | |
| 653 | a Degradation predictions | |
| 653 | a Health status | |
| 653 | a Impedance spectrum | |
| 653 | a Partial voltage | |
| 653 | a Reliable operation | |
| 653 | a Safe operation | |
| 653 | a Forecasting | |
| 700 | 1 | a Xiong, R.u School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China4 aut |
| 700 | 1 | a Meng, X.u Contemporary Amperex Technology Co.Limited, Ningde, 352106, China4 aut |
| 700 | 1 | a Deng, X.u School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China4 aut |
| 700 | 1 | a Li, Hailong,d 1976-u Mälardalens universitet,Framtidens energi4 aut0 (Swepub:mdh)hli01 |
| 700 | 1 | a Sun, F.u School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China4 aut |
| 710 | 2 | a School of Mechanical Engineering, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing, 100081, Chinab Contemporary Amperex Technology Co.Limited, Ningde, 352106, China4 org |
| 773 | 0 | t eTransporationd : Elsevierg 22q 22x 2590-1168 |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-68071 |
| 856 | 4 8 | u https://doi.org/10.1016/j.etran.2024.100347 |
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