用于电池退化诊断的物理信息机器学习：最先进方法的比较
Physics-Informed Machine Learning for Battery Degradation Diagnostics: A Comparison of State-of-the-Art Methods
AI总结：
本文比较四种PIML建模方法，基于24个植入级电芯四年循环老化数据，评估其利用短期数据预测组件级长期退化的准确性、复杂度、可实现性与外推能力。
摘要：
在锂离子电池老化过程中监测其内部组件的健康状况，对于优化电芯设计和使用控制策略至关重要。然而，量化组件级退化通常需要使许多电芯老化，并在整个老化测试期间对其进行破坏性分析，这将可量化退化的范围限制在测试条件和持续时间内。幸运的是，用于建模和预测电池健康状态的物理信息机器学习（PIML）近期进展表明，利用物理知识，仅借助短期老化测试数据构建模型来预测锂离子电池电芯主要组件的长期退化具有可行性。本文提出了四种构建物理信息机器学习模型的方法，并从准确性、复杂性、实现难易程度以及向未测试条件外推的能力方面对其进行全面比较。我们深入研究了每种物理信息机器学习方法的细节，提供了在小型电池老化数据集上实现这些方法的具体见解。本研究利用了24个植入级锂离子电池电芯在四年间经受不同温度和C倍率的长期循环老化数据。本文旨在促进选择合适的物理信息机器学习方法，以利用短期老化数据预测锂离子电池的长期退化，同时为一般预测目的应在何时选择何种方法提供见解。
英文摘要：
Monitoring the health of lithium-ion batteries' internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell's major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.
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