A convolution and memory network-based spatiotemporal model for thermal dynamics of multiple heat sources and its application in serial-connected lithium batteries

Many industrial processes belong to complex thermal system coupled by multiple heat sources, in which the energy distribution and transmission for each heat source is closely related to its adjacent neighbors, and the thermal dynamics have obvious nonlinear time-series characteristics, these factors limit the modeling and predict accuracy for this type of systems in actual practice. In this paper, a convolution and memory network-based spatiotemporal modeling approach is proposed to model the nonlinear spatial features and temporal dynamics of these heating systems. First, a deformation convolutional neural network (D-CNN) strategy is developed to extract the spatial features, it designs a deformable kernel matrix for convolution layer to obtain the spatial multi-scale context information of the DPS, and the feature order is reduced by pooling operation. Then, a cascade long-short time memory network (LSTM) model is constructed to model the temporal dynamics of the time-series data. In this model, the sliding window method is used to obtain the sequential dataset for several sampling time, which is taken as the input of each LSTM unit to reconstruct the nonlinear temporal dynamics in this type of systems. The effect of the proposed model for spatiotemporal dynamics is verified by the experiment of serial-connected lithium batteries (S-LIBs), and the result shows that the proposed model has smaller modeling errors for spatiotemporal dynamics than the other two methods.