Continuous Feature Reconstruction-Based Spatiotemporal Modeling of Distributed Thermal Processes Under Limited Sensing

Numerous industrial thermal processes and fluid dynamics systems can be described by distributed parameter systems (DPSs), where the inputs, outputs, and process variables vary in space and time. In this research, a continuous feature reconstruction-based spatiotemporal modeling method is introduced for DPSs under limited sensing scenarios. Initially, a discrete space completion approach is created to recuperate the spatiotemporal patterns of nonmonitored locations by limited sensors. Then, a continuous feature reconstruction method is designed for deriving continuous spatial basis functions (SBFs), which are the intrinsic characteristics of DPSs. The identification and adjustment of the nonlinear temporal model are carried out via the long short-term memory neural network. Eventually, the amalgamation of the derived SBFs and the temporal model results in a spatially continuous representation. The proposed method reduces the reliance of traditional first-principle methods on governing equations and minimizes the need for extensive sensor arrays in purely data-driven approaches. Experimental tests conducted on a pouch-type Li-ion battery demonstrate the effectiveness and superiority of the proposed spatiotemporal modeling method under limited sensing.