论文标题
考虑到噪声的特征电化学测量值的连续电池模型的贝叶斯参数化
Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise
论文作者
论文摘要
物理化学连续电池电池模型通常通过手动拟合参数化，并依赖于研究人员的个人专业知识。在本文中，我们介绍了一种计算机算法，该算法直接利用电池研究人员的体验可重复地从实验数据中提取信息。我们扩展了贝叶斯优化（BOLFI），并具有期望传播（EP），以创建适用于模块化连续电池模型的黑盒优化器。标准方法将原始数据中的实验数据与模型模拟进行了比较。通过将数据分为基于物理的特征，我们的数据驱动方法使用的模拟量较少。为了进行验证，我们处理全细胞GITT测量值，以表征两种电极的扩散性。我们的算法使实验者和理论家能够调查，验证和记录他们的见解。我们打算该算法成为实验数据库无障碍评估的工具。
Physico-chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black-box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics-based features, our data-driven approach uses orders of magnitude less simulations. For validation, we process full-cell GITT measurements to characterize the diffusivities of both electrodes non-destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases.
