基于深度学习的不同老化条件下电池寿命精确预测
Accurate battery lifetime prediction across diverse aging conditions with deep learning
AI总结：
提出一种通用深度学习框架，通过结合单电池与电池间特征差异，在涵盖401个电池的综合基准上，利用前100个循环实现10%预测误差，显著提升了跨老化条件及低资源条件下的电池寿命预测准确性与鲁棒性。
摘要：
在早期循环中准确预测电池寿命对电池研发及众多下游应用具有巨大价值。由于电极材料、操作条件和工作环境等多种条件共同决定了复杂的容量衰减行为，该任务极具挑战性。然而，现有的预测方法是在有限的老化条件下开发和验证的，导致其对不同老化条件的适应性存疑，且无法充分利用在不同条件下收集的历史数据。在此，我们提出了一种通用的深度学习方法，该方法能够适应各种老化条件，并通过利用丰富条件下的数据来促进低资源条件下的有效学习。我们的关键发现是，结合电池间的特征差异，而不仅仅是考虑单电池特征，可以显著提高电池寿命预测的准确性及其跨条件鲁棒性。因此，我们开发了一个同时包含单电池和电池间建模的整体学习框架。我们构建了一个综合基准进行评估，涵盖了在168种循环条件下使用5种常见电极材料的401个电池。我们展示了在不同老化条件下的卓越学习能力，仅使用前100个循环就实现了10%的预测误差，并且在促进低资源学习方面，在许多情况下将单电池建模的误差降低了一半。更广泛地说，通过打破不同老化条件之间的学习边界，我们的方法可以显著加速锂离子电池的开发和优化。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
