The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network
Abstract
Abstract To improve state-of-health (SOH) estimation and remaining useful life (RUL) prediction, a prognostic framework shared by multiple batteries is proposed. A variant long-short-term memory (LSTM) neural network (NN), called AST-LSTM NN, is designed to guarantee the performance of proposed framework. Firstly, the input and forget gates are coupled by a fixed connection, which leads simultaneous determination of old information and new data. Secondly, the element-wise product of the new inputs and the historical cell states is conducted for screening out more beneficial information. Thirdly, a peephole connection from the “constant error carousel” (CEC) is added into the output gate to shield the unwanted error signals. AST-LSTM NNs, with mapping structures of many-to-one and one-to-one, are well-trained separately for the prediction of SOH and RUL. Compared with other data-driven methods, the experiments carried on NASA dataset demonstrate our method hits lower average root mean square, 0.0216, and conjunct error, 0.0831, for SOH and RUL, respectively.
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Cites
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State estimation for advanced battery management: Key challenges and future trends
Hu, X., Feng, F., Liu, K., Zhang, L., Xie, J., & Liu, B. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109334 · Cited by 857
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A novel state of health estimation method of Li-ion battery using group method of data handling
Wu, J., Wang, Y., Zhang, X., & Chen, Z. (2016) · Journal of Power Sources · doi:10.1016/j.jpowsour.2016.07.065 · Cited by 235
Qin, T., Zeng, S., & Guo, J. (2015) · Microelectronics Reliability · doi:10.1016/j.microrel.2015.06.133 · Cited by 176
Cited by
Wang, Z., Feng, G., Zhen, D., Gu, F., & Ball, A. (2021) · Energy Reports · doi:10.1016/j.egyr.2021.08.113 · Cited by 441
Ma, Y., Shan, C., Gao, J., & Chen, H. (2022) · Energy · doi:10.1016/j.energy.2022.123973 · Cited by 351
Wang, S., Wu, F., Takyi-Aninakwa, P., Fernandez, C., Stroe, D.-I., & Huang, Q. (2023) · Energy · doi:10.1016/j.energy.2023.128677 · Cited by 281
Yang, N., Song, Z., Hofmann, H., & Sun, J. (2022) · Journal of Energy Storage · doi:10.1016/j.est.2021.103857 · Cited by 276
Battery Safety: Machine Learning-Based Prognostics
Zhao, J., Feng, X., Pang, Q., Fowler, M., Lian, Y., Ouyang, M., & Burke, A. F. (2024) · Progress in Energy and Combustion Science · doi:10.1016/j.pecs.2023.101142 · Cited by 216
Metadata
- Authors
- Li, P., Zhang, Z., Xiong, Q., Ding, B., Hou, J., Luo, D., Rong, Y., & Li, S.
- Venue
- Journal of Power Sources
- Publisher
- Elsevier BV
- Published
- 2020-05
- Volume
- 459
- Pages
- 228069
- ISSN
- 0378-7753 (ISSN-L)
- OpenAlex
- W3013372615
- Cited by
- 457
- References
- 9
- FWCI
- 23.31
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, P., Zhang, Z., Xiong, Q., Ding, B., Hou, J., Luo, D., Rong, Y., & Li, S. (2020). State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network. Journal of Power Sources. https://doi.org/10.1016/j.jpowsour.2020.228069
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