The integration of diverse fields enables a more profound exploration of the complex phenomena associated with lithium-ion battery health. In summary, the pioneering work by Wang, HK., Dai, X., and Ran, Q. lays a robust foundation for the future of lithium-ion battery management. Subject of Research: Lithium-ion battery remaining useful life predictionArticle Title: Lithium-ion battery remaining useful life prediction based on dynamic filter frequency mixing learner and dual-stream MambaArticle References:Wang, HK., Dai, X., Ran, Q. et al. Lithium-ion battery remaining useful life prediction based on dynamic filter frequency mixing learner and dual-stream Mamba. https://doi.org/10.1007/s11581-025-06715-1Image Credits: AI GeneratedDOI: https://doi.org/10.1007/s11581-025-06715-1Keywords: lithium-ion batteries, remaining useful life, prediction, machine learning, dynamic filtering, dual-stream analysis, battery management systems, sustainability, energy storage.