The DOI matches its Crossref record; 129 of the 129 references it cites were found in a registry or an index.
A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries
Open accessAbstract
As widely used for secondary energy storage, lithium-ion batteries have become the core component of the power supply system and accurate remaining useful life prediction is the key to ensure its reliability. Because of the complex working characteristics of lithium-ion batteries as well as the model parameter changing along with the aging process, the accuracy of the online remaining useful life prediction is difficult but urgent to be improved for the reliable power supply application. The deep learning algorithm improves the accuracy of the remaining useful life prediction, which also reduces the characteristic testing time requirement, providing the possibility to improve the power profitability of predictive energy management. This article analyzes, reviews, classifies, and compares different adaptive mathematical models on deep learning algorithms for the remaining useful life prediction. The features are identified for the modeling ability, according to which the adaptive prediction methods are classified. The specific criteria are defined to evaluate different modeling accuracy in the deep learning calculation procedure. The key features of effective life prediction are used to draw relevant conclusions and suggestions are provided, in which the high-accuracy deep convolutional neural network — extreme learning machine algorithm is chosen to be utilized for the stable remaining useful life prediction of lithium-ion batteries.
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A review on rapid state of health estimation of lithium-ion batteries in electric vehicles
Wang, Z., Zhao, X., Fu, L., Zhen, D., Gu, F., & Ball, A. D. (2023) · Sustainable Energy Technologies and Assessments · doi:10.1016/j.seta.2023.103457 · Cited by 107
Repurposing Second-Life EV Batteries to Advance Sustainable Development: A Comprehensive Review
Akram, M. N., & Abdul-Kader, W. (2024) · Batteries · doi:10.3390/batteries10120452 · Cited by 50
Berghout, T., Benbouzid, M., Amirat, Y., & Yao, G. (2023) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2023.3237726 · Cited by 29
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Metadata
- Authors
- Wang, S., Jin, S., Bai, D., Fan, Y., Shi, H., & Fernandez, C.
- Venue
- Energy Reports
- Publisher
- Elsevier BV
- Published
- 2021-11
- Volume
- 7
- Pages
- 5562-5574
- ISSN
- 2352-4847 (ISSN-L)
- OpenAlex
- W3199081584
- Cited by
- 258
- References
- 129
- FWCI
- 16.11
- Type
- journal article
- Language
- en
- Open access
- gold
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- Wang, S., Jin, S., Bai, D., Fan, Y., Shi, H., & Fernandez, C. (2021). A critical review of improved deep learning methods for the remaining useful life prediction of lithium-ion batteries. Energy Reports. https://doi.org/10.1016/j.egyr.2021.08.182
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