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

Puntos clave

• 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.