发表机构
University of Notre Dame; The University of Texas at Dallas; Cornell University（圣母大学; 德克萨斯大学达拉斯分校; 康奈尔大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出PiDDM框架，将退化物理纳入神经网络训练，在55块电池的6种协议数据及外推任务中，其预测误差与均方误差均优于基线模型，可实现准确且物理一致的电池SOH预测。
AI 中文摘要
准确预测锂离子电池健康状态（SOH）对可靠的储能运行至关重要。然而，纯数据驱动模型在不同循环协议间的泛化能力较差，且长期外推时会产生物理上不合理的行为。我们开发了一种用于电池SOH预测的物理信息可微退化建模框架PiDDM，该框架将与固体电解质界面生长及锂库存损失相关的经验性阿伦尼乌斯退化动力学纳入训练目标，促使模型在不同运行条件下产生物理一致的容量衰减。该框架使用包含55块电池的公开数据集进行评估，这些电池在6种运行协议下循环。PiDDM在评估模型中实现了最低的平均预测误差，与多层感知机及基线物理信息神经网络相比，大幅降低了均方误差。对于外推任务，模型在每块电池循环寿命的前90%数据上训练，在未见过的最后10%数据上评估，PiDDM捕捉到了加速的寿命末期退化，同时避免了基线模型产生的非物理容量再生。这些结果表明，将退化物理纳入神经网络训练可提高预测准确性和物理一致性，为实用的电池健康监测提供了一种有前景的方法。
英文摘要
Accurate prediction of lithium-ion battery state of health (SOH) is essential for reliable energy storage operation. However, purely data-driven models may generalize poorly across cycling protocols and produce physically implausible behavior during long-term extrapolation. We developed a physics-informed differentiable degradation modeling framework (PiDDM) for battery SOH prediction. PiDDM incorporates empirical Arrhenius degradation kinetics associated with solid electrolyte interphase growth and loss of lithium inventory into the training objective, encouraging physically consistent capacity fade under diverse operating conditions. The framework was evaluated using a public dataset of 55 batteries cycled under six operating protocols. PiDDM achieved the lowest average prediction error among the evaluated models and substantially reduced mean squared error relative to a multilayer perceptron and a baseline physics-informed neural network. For extrapolation, the models were trained on the first 90% of each battery's cycle life and evaluated on the unseen final 10%. PiDDM captured accelerated end-of-life degradation while avoiding the nonphysical capacity regeneration produced by the baseline models. These results show that incorporating degradation physics into neural network training improves predictive accuracy and physical consistency, providing a promising approach for practical battery health monitoring.
