发表机构
Loughborough University; Oxford Brookes University（拉夫堡大学; 牛津布鲁克斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对锂离子电池SoH预测等领域现象学模型参数混杂的问题，提出结合超参数最优选择的正则化迭代广义最小二乘方法，可高效拟合异方差与序列相关数据，模拟验证了其有效性。
AI 中文摘要
在当前以经验方法为主的一些领域，如锂离子电池的健康状态（SoH）预测，由准物理思维驱动的现象学模型包含需从实验数据中估计的参数。这类模型的结构常产生完全或部分混杂的参数，难以甚至无法可靠估计。为保留所需的模型形式，同时改善该问题的数值条件，我们引入了一种岭回归方案。基于模型性能的信息论度量，我们提供了一种自动方法，可在每次迭代中优化岭回归超参数。所提出的公式需要定点迭代来求解超参数。给定合适的初始值，分析表明收敛速度极快。最优超参数选择机制被整合进一种高效的正则化迭代广义最小二乘机制，该机制可根据需要拟合异方差和序列相关数据。模拟结果证实了该整体方法的有效性。
英文摘要
In some fields currently dominated by empirical approaches, such as state of health (SoH) prediction for lithium-ion batteries, phenomenological models motivated by quasi-physical thinking contain parameters to be estimated from experimental data. Often the structure of such models yields fully or partially confounded parameters, which are difficult or even impossible to estimate reliably. To preserve the desired model formulation and simultaneously improve the numerical conditioning for the problem we introduce a ridge regression scheme. An automated method is provided, based on information theoretic measures of model performance, which optimises the ridge regression hyper-parameter at each iteration. The formulae presented require fixed point iteration to solve for the hyper-parameter. Given a suitable starting value, analysis demonstrates convergence is very rapid. The optimal hyper-parameter selection mechanism is incorporated within an efficient regularised iterative generalised least squares mechanism, capable of fitting both heteroscedastic and serially correlated data as required. Simulation confirms the efficacy of the overall method.
Comments18 pages, 5 figures, 2 tables
