April 10, 2026 article Open access Bridging data-driven and physics-based models for lithium-ion battery state analysis and future performance assessment via active material volume fraction Energy and AI DOI: 10.1016/j.egyai.2026.100749 Full text (OA) OpenAlex Authors 0 Author list not loaded yet. Cited by 0 stored of 2 References 3 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 Physics-informed neural networks for electrode-level state estimation in lithium-ion batteries 2021 Journal of Power Sources article Engineering Advanced Battery Technologies Research cited by 6 patents 149 citations Innovations and prognostics in battery degradation and longevity for energy storage systems 2025 Journal of Energy Storage article Engineering Advanced Battery Technologies Research Open access 39 citations The future of battery data and the state of health of lithium-ion batteries in automotive applications 2024 Communications Engineering article Engineering Advanced Battery Technologies Research Open access 19 citations 3 results