Distributed Thermal Monitoring for Large-Format Li-Ion Battery Under Limited Sensing

Thermal monitoring is critical to the safety of electric vehicles. Due to the uneven surface temperature of large-format lithium-ion (Li-ion) batteries, traditional lump-based thermal monitoring methods cannot capture two-dimensional (2-D) spatiotemporal thermodynamics. This article proposes a distributed thermal monitoring framework to solve this problem. It contains two parts. In the modeling part, the spectral method is used to establish the globally distributed temperature field of the pouch cell using only four sensors. In the fault detection part, there are two stages: 1) in the offline training stage, the 2-D battery thermal process is first decomposed into basis functions (BFs) and time coefficients using the spectral method. The time coefficients are further decomposed by independent component analysis (ICA). Then, the dominant temporal components are formed as the monitoring statistic, which is used to derive the confidence bound through kernel density estimation (KDE) and 2) in the online detection stage, the thermal fault can be detected in real-time by comparing the updated monitoring statistic with the confidence bound. Simulations and experiments on a pouch-type Li-ion battery are conducted to verify the effectiveness of the proposed method.