标题： 伪二维模型的可辨识性分析与单粒子模型辅助的参数估计 显示英文标题

标题： Identifiability Analysis of a Pseudo-Two-Dimensional Model & Single Particle Model-Aided Parameter Estimation

摘要： 本贡献提出了一种参数识别方法，用于在伪二维（P2D）电池模型中准确且快速地估计模型参数。该方法包含三个关键要素。首先，检查用于识别的数据，并将其中需要捕捉的特定特征包含在模型中。其次，分析P2D模型以评估物理模型参数的可识别性，并提出可缓解潜在问题的替代参数化方法。最后，考虑多种运行条件，这些条件激发不同的电池动态，从而可以相应地使用不同的低阶电池模型。结果表明，在低电流条件下，使用低阶模型获得的参数估计速度至少比使用P2D模型快500倍，但误差是其两倍。然而，如果必须保证准确性，这些估计的参数可以用于初始化P2D模型，并将识别时间缩短一半。 显示英文摘要

摘要： This contribution presents a parameter identification methodology for the accurate and fast estimation of model parameters in a pseudo-two-dimensional (P2D) battery model. The methodology consists of three key elements. First, the data for identification is inspected and specific features herein that need to be captured are included in the model. Second, the P2D model is analyzed to assess the identifiability of the physical model parameters and propose alternative parameterizations that alleviate possible issues. Finally, diverse operating conditions are considered that excite distinct battery dynamics which allows the use of different low-order battery models accordingly. Results show that, under low current conditions, the use of low-order models achieve parameter estimates at least 500 times faster than using the P2D model at the expense of twice the error. However, if accuracy is a must, these estimated parameters can be used to initialize the P2D model and perform the identification in half of the time.