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
该研究针对实验室循环老化的10个电池单体，采用一阶线性化非线性重复测量方法建模，通过正则化迭代广义最小二乘方案，实现了SoH在[0,20]区间内±0.191%的精准预测。
AI 中文摘要
本文描述了一阶线性化非线性重复测量方法在实验室受控条件下生成的电池单体老化曲线分析中的应用。该模型的主要优势在于能反映数据中的明显结构，因此它是一个两分量方差模型：老化曲线内的变异（测量噪声）以及老化曲线间的变异（测试间或单体间变异）。采用带最优超参数重估的新型正则化迭代广义最小二乘参数识别方案来辨识该分层非线性模型。训练数据包含10个单体的SoH曲线，这些单体在固定箱内环境温度25℃下，经各种恒定充放电电流循环老化。每个单体的SoH曲线用简单幂律表达式建模，而老化参数的变异用单节点三次B样条建模。对于SOH∈[0,20]，SoH的预测精度可达±0.191%。
英文摘要
This document describes the application of a first order linearised nonlinear repeated measurements approach to the analysis of battery cell ageing profiles generated under controlled conditions in a laboratory. The primary advantage of the model is it reflects the obvious structure in the data. Consequently, it is a two-component of variance model: variation within ageing profiles (measurement noise) and variation among ageing profiles (test-to-test or cell-to-cell) variation. Novel regularised iterative generalised least squares parameter identification schemes, with optimal hyper-parameter re-estimation, are used to identify the hierarchical nonlinear model. The training data comprised $SoH$ profiles for 10 cells aged at various constant discharge and charge current cycles at a fixed chamber environmental temperature of 25 [$^\circ$C]. Each cell $SoH$ profile is modelled using a simple power law expression, whereas the variation in ageing parameters is modelled using a single knot cubic B-spline. $SoH$ is accurately predicted to $\pm 0.191\%$ for $SOH \in [0,20]$.
Comments17 pages, 9 figures, 2 tables
