贝叶斯参数估计在含电解液动力学的锂离子电池单粒子模型中的应用
Bayesian Parameter Estimation Applied to the Li-ion Battery Single Particle Model with Electrolyte Dynamics
AI总结：
本文提出贝叶斯参数估计方法，分析锂离子电池单粒子模型参数的可辨识性，发现全局估计下扩散系数和迁移数方差小，局部估计不确定性大。
摘要：
本文提出了一种针对锂离子电池模型的贝叶斯参数估计方法及可辨识性分析，以确定模型参数子集的唯一性、评估其灵敏度并量化其不确定性。该分析基于含电解液动力学的单粒子模型，该模型通过渐近分析（包括电极平均项）从Doyle-Fuller-Newman模型严格推导而来。贝叶斯方法能够估计复杂的目标分布，从而实现对参数空间的全局分析。分析聚焦于辨识问题：（i）在局部情况下，针对一组离散的准稳态荷电状态；（ii）在全局情况下，与荷电状态的连续变化进行比较。该方法的性能通过多种电流激励下的多次数值模拟生成的合成数据进行了评估。我们表明，在全局情况下，各种扩散系数以及迁移数可以以较小的方差进行估计，但在局部估计情况下不确定性要大得多。这对参数可能随荷电状态或其他潜在变量变化的估计也具有重要意义。
英文摘要：
This paper presents a Bayesian parameter estimation approach and identifiability analysis for a lithium-ion battery model, to determine the uniqueness, evaluate the sensitivity and quantify the uncertainty of a subset of the model parameters. The analysis was based on the single particle model with electrolyte dynamics, rigorously derived from the Doyle-Fuller-Newman model using asymptotic analysis including electrode-average terms. The Bayesian approach allows complex target distributions to be estimated, which enables a global analysis of the parameter space. The analysis focuses on the identification problem (i) locally, under a set of discrete quasi-steady states of charge, and in comparison (ii) globally with a continuous excursion of state of charge. The performance of the methodology was evaluated using synthetic data from multiple numerical simulations under diverse types of current excitation. We show that various diffusivities as well as the transference number may be estimated with small variances in the global case, but with much larger uncertainty in the local estimation case. This also has significant implications for estimation where parameters might vary as a function of state of charge or other latent variables.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
