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.

翻译：物理化学连续电池模型通常依赖研究人员手动拟合参数，这一过程高度依赖个体专家的经验。本文提出一种计算机算法，可直接利用电池研究人员的经验，从实验数据中可重复地提取信息。我们通过期望传播（EP）扩展贝叶斯优化（BOLFI），构建适用于模块化连续电池模型的黑箱优化器。传统方法将原始实验数据整体与模型模拟进行对比；而我们的数据驱动方法通过将数据分解为基于物理的特征，可将模拟次数降低数个数量级。为验证有效性，我们处理全电池GITT测量数据，以非破坏性方式表征两个电极的扩散系数。本算法使实验研究者和理论研究者能够探究、验证并记录其见解。我们旨在将该算法打造为可便捷评估实验数据库的工具。