用重复测量建模电池老化，精准预测健康度。
A Repeated Measurements Approach to $SoH$ Battery Modelling of Cyclic Aged Data in a Laboratory Environment
- 基于重复测量的分层非线性模型，分离测试间与测试内差异。
- 在20%健康度范围内预测误差小于±0.191%。
- 适合实验室老化数据建模，尤其关注电池一致性分析。
本文采用一阶线性化非线性重复测量方法，分析实验室环境下受控条件下的电池老化数据。模型具有双成分方差结构：反映单个老化曲线内的测量噪声，以及不同电池间的测试-测试或单元-单元差异。提出新型正则化迭代广义最小二乘参数识别方法，并实现最优超参数重估。训练数据包含10个电池在25℃恒温环境、不同充放电电流循环下的SoH（健康度）曲线。每个电池的SoH变化采用简单幂律表达式建模，老化参数的波动则通过单节点三次B样条拟合。模型在SoH∈[0,20]范围内预测精度达±0.191%。
原文摘要 · Abstract (English)
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 $SOH \in [0,20]$.
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