  Accurately predicting the lifetime of battery cells in early cycles holds
tremendous value for battery research and development as well as numerous
downstream applications. This task is rather challenging because diverse
conditions, such as electrode materials, operating conditions, and working
environments, collectively determine complex capacity-degradation behaviors.
However, current prediction methods are developed and validated under limited
aging conditions, resulting in questionable adaptability to varied aging
conditions and an inability to fully benefit from historical data collected
under different conditions. Here we introduce a universal deep learning
approach that is capable of accommodating various aging conditions and
facilitating effective learning under low-resource conditions by leveraging
data from rich conditions. Our key finding is that incorporating inter-cell
feature differences, rather than solely considering single-cell
characteristics, significantly increases the accuracy of battery lifetime
prediction and its cross-condition robustness. Accordingly, we develop a
holistic learning framework accommodating both single-cell and inter-cell
modeling. A comprehensive benchmark is built for evaluation, encompassing 401
battery cells utilizing 5 prevalent electrode materials across 168 cycling
conditions. We demonstrate remarkable capabilities in learning across diverse
aging conditions, exclusively achieving 10% prediction error using the first
100 cycles, and in facilitating low-resource learning, almost halving the error
of single-cell modeling in many cases. More broadly, by breaking the learning
boundaries among different aging conditions, our approach could significantly
accelerate the development and optimization of lithium-ion batteries.
