The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network
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Artificial neural networks: fundamentals, computing, design, and application
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Batteries and fuel cells for emerging electric vehicle markets
Cano, Z. P., Banham, D., Ye, S., Hintennach, A., Lu, J., Fowler, M., & Chen, Z. (2018) · Nature Energy · doi:10.1038/s41560-018-0108-1 · Cited by 2,873
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Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation
Rodriguez, J. D., Perez, A., & Lozano, J. A. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2009.187 · Cited by 1,890
Predicting residential energy consumption using CNN-LSTM neural networks
Kim, T.-Y., & Cho, S.-B. (2019) · Energy · doi:10.1016/j.energy.2019.05.230 · Cited by 1,469
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Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries
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Cited by
Wang, S., Fan, Y., Jin, S., Takyi-Aninakwa, P., & Fernandez, C. (2023) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2022.108920 · Cited by 554
Gu, X., See, K. W., Li, P., Shan, K., Wang, Y., Zhao, L., Lim, K. C., & Zhang, N. (2023) · Energy · doi:10.1016/j.energy.2022.125501 · Cited by 373
Ma, Y., Shan, C., Gao, J., & Chen, H. (2022) · Energy · doi:10.1016/j.energy.2022.123973 · Cited by 351
Prognostics of battery capacity based on charging data and data-driven methods for on-road vehicles
Deng, Z., Xu, L., Liu, H., Hu, X., Duan, Z., & Xu, Y. (2023) · Applied Energy · doi:10.1016/j.apenergy.2023.120954 · Cited by 268
Shrivastava, P., Naidu, P. A., Sharma, S., Panigrahi, B. K., & Garg, A. (2023) · Journal of Energy Storage · doi:10.1016/j.est.2023.107159 · Cited by 205
Metadata
- Authors
- Cheng, G., Wang, X., & He, Y.
- Venue
- Energy
- Publisher
- Elsevier BV
- Published
- 2021-10
- Volume
- 232
- Pages
- 121022
- ISSN
- 0360-5442 (ISSN-L)
- OpenAlex
- W3164975616
- Cited by
- 258
- References
- 49
- FWCI
- 12.74
- Type
- journal article
- Language
- en
- Open access
- closed
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- This page
- CoChat. (2026). Citation check: Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network. https://app.cochat.ai/papers/10.1016/j.energy.2021.121022
- The paper
- Cheng, G., Wang, X., & He, Y. (2021). Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network. Energy. https://doi.org/10.1016/j.energy.2021.121022
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
