March 18, 2020 article Open access A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery IEEE Transactions on Industrial Electronics DOI: 10.1109/tie.2020.2973876 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 4 stored of 842 Search Sort Most cited Newest Oldest Patent citations Title Any typearticle review book-chapter conference-paper preprint dissertation book dataset other Any fieldAgricultural and Biological Sciences Arts and Humanities Biochemistry, Genetics and Molecular Biology Business, Management and Accounting Chemical Engineering Chemistry Computer Science Decision Sciences Dentistry Earth and Planetary Sciences Economics, Econometrics and Finance Energy Engineering Environmental Science Health Professions Immunology and Microbiology Materials Science Mathematics Medicine Neuroscience Nursing Pharmacology, Toxicology and Pharmaceutics Physics and Astronomy Psychology Social Sciences Veterinary Open access Data sufficiency for transferable lithium-ion battery periodical SOH estimation under resource constraints 2025 Cell Reports Physical Science article Engineering Advanced Battery Technologies Research Open access 12 citations Large-scale field data-based battery aging prediction driven by statistical features and machine learning 2023 Cell Reports Physical Science article Engineering Advanced Battery Technologies Research Open access 53 citations A multi-scale learning approach for remaining useful life prediction of lithium-ion batteries based on variational mode decomposition and Monte Carlo sampling 2023 Energy article Engineering Advanced Battery Technologies Research Open access 58 citations Transferable data-driven capacity estimation for lithium-ion batteries with deep learning: A case study from laboratory to field applications 2023 Applied Energy article Engineering Advanced Battery Technologies Research 57 citations 4 results References 0