Real-time data reconstruction-based joint estimation of state-of-charge and state-of-health of lithium-ion batteries using quantitative feature informed deep learning framework | Synapse
March 3, 2026
Real-time data reconstruction-based joint estimation of state-of-charge and state-of-health of lithium-ion batteries using quantitative feature informed deep learning framework
Key Points
Key outcomes show high accuracy in estimating state-of-charge and state-of-health for lithium-ion batteries, indicating reliable performance.
The proposed deep learning framework utilized real-time data reconstruction techniques, achieving significant advancements in battery diagnostics.
Analysis reveals the capability to use quantitative feature information for enhancing model performance related to battery assessments.
The findings suggest improvements in battery management and longevity, with potential applications in electric vehicles.