Uncovering the impact of battery design parameters on health and lifetime using short charging segments

Frequent fast charging of lithium-ion batteries (LiBs) demands robust health monitoring, not only to ensure long-term performance and user confidence, but also to support emerging applications such as vehicle-to-grid (V2G), where energy flows bidirectionally between EVs and the grid. Without clear insight into how upstream design parameters such as solid-state diffusion coefficient, electrode thickness, particle radius, lithium-ion concentration, and porosity impact battery health in real-world use, however, valuable opportunities to optimize early-stage designs and develop tailored usage strategies to mitigate degradation may be lost. This work proposes a machine learning (ML) framework built on a digital twin model that links key design parameters to real-world behaviors of graphite/nickel–manganese–cobalt–oxide LiBs under a diverse range of fast charging protocols, depths of discharge, and dynamic discharge profiles representative of applications in Nordic climates. The framework infers six key design parameters directly from short charging segments, enabling rapid health prediction within seconds. Notably, this approach improves the robustness of health and lifetime predictions by up to 65% and 69%, respectively, compared to baseline multi-layer perceptron and linear regression models, while also outperforming the baseline random forest model, with a training time of 1 second. The strong physical correlation between capacity variability and three design parameters—solid-state diffusion coefficient, particle radius, and electrode thickness—during fast charging highlights their vital role in determining the degradation pathways. The framework can be readily integrated into upstream workflows and battery management systems, enabling end users to tailor usage patterns and guiding developers toward improved design strategies.