The monitoring of temperature distribution is crucial for advanced battery thermal management. This study proposes a data-driven temperature field prediction method for the pouch cell thermal process, a typical distributed parameter system (DPS). First, empirical spatial basis functions (SBFs) that represent underlying spatial modes of the thermal system are extracted from data snapshots collected offline. Then, we apply the obtained SBFs to the time/space (T/S) separation framework and perform online nonlinear modeling using the partial-node feedback data. On this basis, a dynamics reconstruction strategy is designed for full-node temperature prediction. Experimental studies indicate that the proposed method owns encouraging accuracy and allows minimal sensing configuration. In addition, the error source of the proposed method is systematically analyzed.

中文翻译：

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最小传感下软包电池温度场的数据驱动实时预测

温度分布的监测对于先进的电池热管理至关重要。本研究提出了一种用于软包电池热过程的数据驱动温度场预测方法，这是一种典型的分布式参数系统（DPS）。首先，从离线收集的数据快照中提取表示热系统基础空间模式的经验空间基函数 (SBF)。然后，我们将获得的 SBF 应用于时间/空间 (T/S) 分离框架，并使用部分节点反馈数据进行在线非线性建模。在此基础上，设计了一种用于全节点温度预测的动力学重构策略。实验研究表明，所提出的方法具有令人鼓舞的准确性，并允许最小的传感配置。此外，