Estimation of lithium-ion battery health state using MHATTCN network with multi-health indicators inputs
Author:
Funder
Key Technology Research and Development Program of Shandong Province
Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-69424-1.pdf
Reference37 articles.
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2. Zheng, Y. et al. Thermal state monitoring of lithium-ion batteries: Progress, challenges, and opportunities. Prog. Energy Combust. Sci. 100, 101120 (2024).
3. Li, X., Yu, D., Byg, V. & Ioan, S. The development of machine learning-based remaining useful life prediction for lithium-ion batteries. J. Energy Chem. 82, 103–121 (2023).
4. Yang, Y. et al. An overview of application-oriented multifunctional large-scale stationary battery and hydrogen hybrid energy storage system. Energy Rev. 3, 100068 (2024).
5. Hannan, M. et al. Toward enhanced state of charge estimation of lithium-ion batteries using optimized machine learning techniques. Sci. Rep. 10, 4687 (2020).
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