To facilitate the diagnosis of battery internal short circuit (ISC) using thermal behaviors, this work integrates several thermal effects, including the commonly ignored heat conduction hysteresis and radiation, to elaborate a lumped thermal model. Then, a pseudo-distributed model structure is built up to approximate the characteristics of real batteries by synthesizing multiple isomorphic electrical/thermal submodels with the extreme learning machine network. Besides, three kinds of configurable destructions are conducted to incur ISC consequences. From thermal and electrical model residuals, four ISC features are extracted and the multiclass relevance vector machine is utilized to assess ISC intensity, in which not only qualitative judgments are given but also quantitative confidences can be derived according to the posterior probabilities. Finally, experiments on 18650 Li-ion cells verify the reliability of the synthesized models and suggest that the diagnosis scheme can recognize ISC faults effectively with low grade and state misjudgment rates (14.59% and 3.13%, respectively).

中文翻译：

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使用基于极限学习机的伪分布式模型对 18 650 节电池的内部短路状态进行量化评估

为了使用热行为促进电池内部短路 (ISC) 的诊断，这项工作整合了几种热效应，包括通常被忽略的热传导滞后和辐射，以阐述集总热模型。然后，通过使用极限学习机网络合成多个同构的电/热子模型，构建伪​​分布式模型结构以近似真实电池的特性。此外，进行了三种可配置的破坏以产生 ISC 后果。从热电模型残差中提取四个ISC特征，利用多类相关向量机评估ISC强度，不仅可以给出定性判断，还可以根据后验概率推导出定量置信度。