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.

翻译：精准预测电池在早期循环周期内的寿命，对电池研发及众多下游应用具有巨大价值。这一任务极具挑战性，因为电极材料、运行条件和工作环境等多样条件共同决定了复杂的容量衰减行为。然而，当前预测方法仅在有限老化条件下开发和验证，导致其适应不同老化条件的可靠性存疑，且无法充分利用不同条件下收集的历史数据。本研究提出一种通用深度学习方法，能够适应多种老化条件，并通过利用丰富条件下的数据，在低资源条件下实现高效学习。关键发现是：引入电池间特征差异（而非仅考虑单电池特征）可显著提升电池寿命预测精度及其跨条件鲁棒性。据此，我们构建了融合单电池建模与电池间建模的整体学习框架。为评估模型，我们建立了包含401块电池（涵盖5种主流电极材料、168种循环条件）的综合基准测试。研究表明，该方法具有显著优势：仅利用前100个循环数据即可实现10%的预测误差，且在低资源学习中，许多情况下使单电池建模误差降低近半。更广泛而言，通过打破不同老化条件间的学习壁垒，该方法可显著加速锂离子电池的开发与优化进程。