An interpretable capacity prediction method for lithium-ion battery considering environmental interference
Author:
Funder
National Natural Science Foundation of China
Key Research and Development Program Projects of Heilongjiang Province
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-68886-7.pdf
Reference48 articles.
1. Gao, Y., Jiang, J., Zhang, C., Zhang, W. & Jiang, Y. Aging mechanisms under different state-of-charge ranges and the multi-indicators system of state-of-health for lithium-ion battery with Li(NiMnCo)O2 cathode. J. Power Sources 400, 641–651. https://doi.org/10.1016/j.jpowsour.2018.07.018 (2018).
2. Ge, M.-F., Liu, Y., Jiang, X. & Liu, J. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries. Measurement https://doi.org/10.1016/j.measurement.2021.109057 (2021).
3. Cui, Z., Gao, X., Mao, J. & Wang, C. Remaining capacity prediction of lithium-ion battery based on the feature transformation process neural network. Expert Syst. Appl. https://doi.org/10.1016/j.eswa.2021.116075 (2022).
4. Lin, Z. et al. Estimating the state of health of lithium-ion batteries based on a probability density function. Int. J. Electrochem. Sci. https://doi.org/10.1016/j.ijoes.2023.100137 (2023).
5. Yang, Z., Zhao, X. & Zhang, H. A health assessment method for lithium-ion batteries based on evidence reasoning rules with dynamic reference values. Batteries https://doi.org/10.3390/batteries10010026 (2024).
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