Bridging battery design and health assessment through virtual sensing and physics-informed learning
Supercharging of lithium-ion batteries (LiBs) requires robust health monitoring to ensure durability, safety, and user confidence, particularly for emerging vehicle-to-grid applications with bidirectional energy flows. Yet battery management remains largely disconnected from the material and structural origins of aging, limiting both interpretable health assessment and informed battery design. Here we propose a physics-informed learning framework with virtual sensing that infers hard-to-measure design parameters, including solid-state diffusion coefficient, electrode thickness, ion concentration, and particle size, directly from standard battery management system (BMS) measurements. Acros...
| Authors | Wendi Guo, Søren Byg Vilsen, Daniel Ioan Stroe et al. |
|---|---|
| Primary category | cs.LG |
| Published | 18 July 2026 |
| Last updated at source | 18 July 2026 |
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